Get live statistics and analysis of Ahmad's profile on X / Twitter
ai research, software engineering, infra & hardware, on a mission to build a DGX B300 GPU cluster, i moderate GPUs on r/LocalLLaMA
308following29kfollowers
The Innovator
Ahmad is a tech-savvy AI researcher and software engineer with a passion for hardware and infrastructure, especially GPU clusters. Known for his deep knowledge and hands-on approach, he combines innovation with practical coding skills to push the boundaries of AI and computation. As a moderator on r/LocalLLaMA, he bridges niche communities with cutting-edge AI technology.
Ahmad tweets so frequently he’s probably single-handedly keeping the tweet count above 17,000—at this rate, his keyboard's gonna apply for a restraining order any day now.
Achieved viral success by making complex GPU and AI server setups relatable & trendy with 20k+ likes on a tweet encouraging everyone to think bigger about hardware, proving he can turn hardcore tech talk into viral content.
Ahmad's life purpose is to revolutionize AI infrastructure by building powerful, scalable GPU systems and empowering others to leverage open-source models and innovative computing techniques. He aims to democratize AI through hands-on development and community moderation, fostering a tech ecosystem where innovation thrives.
Ahmad believes in the transformative power of open-source technology, hands-on experimentation, and sharing practical knowledge to drive collective progress. He values efficiency, creativity in problem-solving, and the importance of bridging the gap between theoretical AI research and real-world applications.
He excels at integrating complex hardware with AI software, combined with an ability to communicate technical concepts humorously and engagingly, making him both a pioneer and an influencer in niche AI communities.
His intensive focus on tech and infrastructure might make him less approachable to casual users or audiences outside the AI/engineering sphere, potentially limiting broader mainstream engagement.
To grow Ahmad’s audience on X, he should blend his technical posts with more simplified, user-friendly content—think quick tips, memes, or relatable tech humor—to draw in non-experts while maintaining deep-dive threads for core followers. Collaborations with popular AI influencers or sharing behind-the-scenes looks at his GPU cluster build could also boost engagement.
Fun fact: Ahmad’s dedication is so intense that he’s on a mission to build a DGX B300 GPU cluster, proving he’s not just theorizing about AI but physically building the future one GPU at a time.
Cooperative network for verifiable compute @novanet_zkp (DeSCI) I work where AI meets cryptography: @kinic_app . “What do you do for others? #runawayZK
The Blockchain for gaming and entertainment, offering 1M+ TPS, sub-second finality and sub-cent fees. Follow @SomniaEco and @somniaGames_ for ecosystem updates!
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be me\n> curious how X decides who goes viral and who gets shadowbanned into oblivion\n\n> read the source code. all of it. 400,000 lines\n> it's a mess. it's a masterpiece. it's a threat model disguised as a social network\n\n> proceed to get 7M impressions and 7k followers in 9 days\n\n> i have *seen* the algorithm\n> here's how to make it your slave\n\n> X is a game\n> rules are secret\n> stakes are your visibility\n> you win by:\n\n> replying to replies (replyguymaxxing)\n> baiting profile clicks (profilevisitmax)\n> getting bookmarked like you're the Dead Sea Scrolls\n> not getting blocked or muted (instant debuff)\n> spacing tweets out (diversity filters will kneecap your burst posts)\n\n> every 6 hours, your tweet loses 50% of its power\n> decay rate is brutal\n> either pop off early or perish\n\n> some actions boost you:\n\n> replies\n> retweets\n> likes\n> bookmarks\n> follows after a tweet\n> long watch time (10+ sec = algorithmic arousal)\n> text read for 2+ seconds = good 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":50,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":73,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":237,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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GPU actually matters for LLMs in 2025? what specs move the needle for inference, for training? why does bandwidth matter more than FLOPS?\n\nthe core specs that move the needle for LLMs\n> compute FLOPS (FP16/BF16/FP8) ≅ how fast you can run matmuls (prefill/training speed)\n> memory bandwidth ≅ how fast you can move tokens/KV-cache (decode ceiling)\n> L2 cache ≅ how quickly you hit L2 vs get stuck on DRAM (latency killer)\n> VRAM ≅ how much model + KV + batch fits on-GPU\n> interconnect ≅ whether multi-GPU setups actually scale (NVLink vs PCIe), and how fast\n> power/TDP ≅ whether you can sustain clocks, or just spike (PSU fried) or/and throttle\n\nlet’s break down this into GPUs\n\nRTX 3090 (Ampere GA102)\n> 24 GB GDDR6X\n> 936 GB/s bandwidth\n> 6 MB L2 cache\n> 142 TFLOPS FP16 tensor (285 sparse)\n> PCIe 4.0 x16 + NVLink (112 GB/s, dual card only, the only consumer card with it)\n> 350W TDP\n\nRTX 4090 (Ada AD102)\n> 24 GB GDDR6X\n> 1.008 TB/s bandwidth\n> 72 MB L2 cache\n> 330 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":1094,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — interactively tune temp/k/p and sample outputs\n> plot entropy vs output diversity as you sweep params\n> nuke temp=0 (argmax): watch repetition\n\n KV Cache (Fast Inference)\n\n> record & reuse KV states; measure speedup vs no-cache\n> build a “cache hit/miss” visualizer for token streams\n> profile cache memory cost for long vs short sequences\n\n Long-Context Tricks: Infini-Attention / Sliding Window\n\n> implement sliding window attention; measure loss on long docs\n> benchmark “memory-efficient” (recompute, flash) variants\n> plot perplexity vs context length; find context collapse point\n\n Mixture of Experts (MoE)\n\n> code a 2-expert router layer; route tokens dynamically\n> plot expert utilization histograms over dataset\n> simulate sparse/dense swaps; measure FLOP savings\n\n Grouped Query Attention\n\n> convert your mini-former to grouped query layout\n> measure speed vs vanilla multi-head on large batch\n> ablate number of groups, plot latency\n\n Normalization & 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for million-token demos\n- interleaved pipelines: +10% throughput, almost free\n- MoE: top-1 routing + expert-choice/hierarchical shuffling = practical at scale\n\nkeep this map in mind: always ask; what’s your bottleneck?\n- weights, work, or state? \n- the right parallelism makes the big thing smaller,\n- without making communication the new bottleneck\n\n- you made it to the end\n- now you know: every parallelism axis is a lever, not a panacea\n- the real move is combining axes for your exact bottleneck; fit, throughput, context, or tail latency\n- next time you see “we train at 8M tokens/sec on 1024 GPUs,” read between the lines: it’s all about how they split the problem\n\ntl;dr: llms are simple, just brutal at scale\n- hardware-aware optimization is survival\n- the real skill is matching bottleneck to the right parallelism; otherwise you’re just moving the 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":32,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":96,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":457,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":1596,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for million-token demos\n- interleaved pipelines: +10% throughput, almost free\n- MoE: top-1 routing + expert-choice/hierarchical shuffling = practical at scale\n\nkeep this map in mind: always ask; what’s your bottleneck?\n- weights, work, or state? \n- the right parallelism makes the big thing smaller,\n- without making communication the new bottleneck\n\n- you made it to the end\n- now you know: every parallelism axis is a lever, not a panacea\n- the real move is combining axes for your exact bottleneck; fit, throughput, context, or tail latency\n- next time you see “we train at 8M tokens/sec on 1024 GPUs,” read between the lines: it’s all about how they split the problem\n\ntl;dr: llms are simple, just brutal at scale\n- hardware-aware optimization is survival\n- the real skill is matching bottleneck to the right parallelism; otherwise you’re just moving the 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":49599,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":115915,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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for inference\n\nRTX PRO 6000 > DGX Spark \n\n> in a 4000 token chat:\n> RTX PRO 6000 is 6–7x faster\n> while only ~1.8x more expensive\n\n> DGX Spark took\n> 100 sec vs 14 sec on Llama 3.1 8B\n> and 13 min vs 100 sec on Llama 3.1 70B\n\nLLM inference is memory‑bound: 1792 GB/s vs 273 GB/s https://t.co/MH768EsdYa","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,99],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1795764095041302529","name":"Sinatras","screen_name":"myainotez","indices":[89,99]}]},"favorited":false,"lang":"en","quoted_status_id_str":"1979381611398316507","quoted_status_permalink":{"url":"https://t.co/NHWmO51ugU","expanded":"https://twitter.com/myainotez/status/1979381611398316507","display":"x.com/myainotez/stat…"},"retweeted":false,"fact_check":null,"id":"1979388291456598386","view_count":2117,"bookmark_count":12,"created_at":1760757918000,"favorite_count":18,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979388291456598386","full_text":"how do we get to small, specialized models?\n\nby doing exactly this\n\ngreat breakdown from @myainotez","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/Li7x5iB6Px","expanded_url":"https://x.com/TheAhmadOsman/status/1979581072368353745/photo/1","id_str":"1979581069096620032","indices":[280,303],"media_key":"3_1979581069096620032","media_url_https":"https://pbs.twimg.com/media/G3jifnuXkAACOOv.jpg","type":"photo","url":"https://t.co/Li7x5iB6Px","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":170,"y":112,"h":46,"w":46}]},"medium":{"faces":[{"x":170,"y":112,"h":46,"w":46}]},"small":{"faces":[{"x":170,"y":112,"h":46,"w":46}]},"orig":{"faces":[{"x":170,"y":112,"h":46,"w":46}]}},"sizes":{"large":{"h":312,"w":475,"resize":"fit"},"medium":{"h":312,"w":475,"resize":"fit"},"small":{"h":312,"w":475,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":312,"width":475,"focus_rects":[{"x":0,"y":0,"w":475,"h":266},{"x":0,"y":0,"w":312,"h":312},{"x":0,"y":0,"w":274,"h":312},{"x":5,"y":0,"w":156,"h":312},{"x":0,"y":0,"w":475,"h":312}]},"media_results":{"result":{"media_key":"3_1979581069096620032"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/Li7x5iB6Px","expanded_url":"https://x.com/TheAhmadOsman/status/1979581072368353745/photo/1","id_str":"1979581069096620032","indices":[280,303],"media_key":"3_1979581069096620032","media_url_https":"https://pbs.twimg.com/media/G3jifnuXkAACOOv.jpg","type":"photo","url":"https://t.co/Li7x5iB6Px","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":170,"y":112,"h":46,"w":46}]},"medium":{"faces":[{"x":170,"y":112,"h":46,"w":46}]},"small":{"faces":[{"x":170,"y":112,"h":46,"w":46}]},"orig":{"faces":[{"x":170,"y":112,"h":46,"w":46}]}},"sizes":{"large":{"h":312,"w":475,"resize":"fit"},"medium":{"h":312,"w":475,"resize":"fit"},"small":{"h":312,"w":475,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":312,"width":475,"focus_rects":[{"x":0,"y":0,"w":475,"h":266},{"x":0,"y":0,"w":312,"h":312},{"x":0,"y":0,"w":274,"h":312},{"x":5,"y":0,"w":156,"h":312},{"x":0,"y":0,"w":475,"h":312}]},"media_results":{"result":{"media_key":"3_1979581069096620032"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979581072368353745","view_count":28397,"bookmark_count":171,"created_at":1760803881000,"favorite_count":425,"quote_count":1,"reply_count":21,"retweet_count":25,"user_id_str":"248951926","conversation_id_str":"1979581072368353745","full_text":"- Xerox \n- invented it all \n- got $1 million in Apple stock \n- entire company gone by 2000 \n- thanks for your service \n\n- Steve Jobs \n- LSD still active \n- go to Xerox PARC \n- see a mouse, windows, GUI \n- brain goes \"holy shit\" \n- say “we’re taking this” \n- engineers: “wait what do you mea—” \n- it’s already in the Mac prototype \n- everyone claps \n- reinvented computing \n- by watching someone else’s demo \n- Jobs: “good artists copy, great artists steal” \n\n- Bill Gates \n- see Mac \n- see Jobs demo it \n- looks suspiciously like Xerox \n- pretend to be impressed \n- “hey Steve, we’d love to build Excel for the Mac” \n- secretly building Windows in the background \n- Gates: “cool. i just stole from the guy who stole” \n- launches it \n- Mac users malding \n- Jobs: \"you STOLE it!\" \n- Gates: \"it's not stealing if you steal it second\" \n- infinite steal chain \n\n- Jobs builds a temple \n- Gates builds a printer driver \n- Jobs makes art \n- Gates makes 95% market share \n\n- Mac has fonts and flair \n- Windows has Minesweeper and 400 registry bugs \n- still wins \n\n- Pirates_of_Silicon_Valley.mp4 \n- Jobs: emotional, dramatic, quoting Picasso \n- Gates: smug, in a sweater, quietly replacing your OS \n\n- moral of the story? \n- demos are dangerous \n- never show your good shit to nerds in sandals \n- they'll steal it and call it being \"nerd sniped\" \n- someone will turn it into a billion-dollar product \n- and call you a visionary while they do it","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979597401435283751","view_count":33285,"bookmark_count":634,"created_at":1760807774000,"favorite_count":432,"quote_count":3,"reply_count":20,"retweet_count":53,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- \"tokenizer\" = the algorithm that splits text into tokens\n- common types: BPE (byte pair encoding), SentencePiece\n- token examples:\n- \"hello\" = 1 token or maybe 2 or 3 tokens\n- \"internationalization\" = 5–8 tokens\n- context window = max tokens model can \"see\" at once (2K, 8K, 32K+)\n- longer context = more VRAM for KV cache, slower decode\n\n- during inference, the model predicts next token\n- by running lots of math on its \"weights\"\n- model weights = billions of learned parameters (the knowledge and patterns from training)\n\n- model parameters: usually billions of numbers (called weights) that the model learns during training\n- these weights encode all the model's \"knowledge\" (patterns, language, facts, reasoning)\n- think of them as the knobs and dials inside the model, specifically computed to recognize what could come next\n- when you run inference, the model uses these parameters to compute its predictions, one token at a time\n\n- every prediction is just: model weights + current sequence → probabilities for what comes next\n- pick a token, append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":73,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":237,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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GPU actually matters for LLMs in 2025? what specs move the needle for inference, for training? why does bandwidth matter more than FLOPS?\n\nthe core specs that move the needle for LLMs\n> compute FLOPS (FP16/BF16/FP8) ≅ how fast you can run matmuls (prefill/training speed)\n> memory bandwidth ≅ how fast you can move tokens/KV-cache (decode ceiling)\n> L2 cache ≅ how quickly you hit L2 vs get stuck on DRAM (latency killer)\n> VRAM ≅ how much model + KV + batch fits on-GPU\n> interconnect ≅ whether multi-GPU setups actually scale (NVLink vs PCIe), and how fast\n> power/TDP ≅ whether you can sustain clocks, or just spike (PSU fried) or/and throttle\n\nlet’s break down this into GPUs\n\nRTX 3090 (Ampere GA102)\n> 24 GB GDDR6X\n> 936 GB/s bandwidth\n> 6 MB L2 cache\n> 142 TFLOPS FP16 tensor (285 sparse)\n> PCIe 4.0 x16 + NVLink (112 GB/s, dual card only, the only consumer card with it)\n> 350W TDP\n\nRTX 4090 (Ada AD102)\n> 24 GB GDDR6X\n> 1.008 TB/s bandwidth\n> 72 MB L2 cache\n> 330 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":1094,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — interactively tune temp/k/p and sample outputs\n> plot entropy vs output diversity as you sweep params\n> nuke temp=0 (argmax): watch repetition\n\n KV Cache (Fast Inference)\n\n> record & reuse KV states; measure speedup vs no-cache\n> build a “cache hit/miss” visualizer for token streams\n> profile cache memory cost for long vs short sequences\n\n Long-Context Tricks: Infini-Attention / Sliding Window\n\n> implement sliding window attention; measure loss on long docs\n> benchmark “memory-efficient” (recompute, flash) variants\n> plot perplexity vs context length; find context collapse point\n\n Mixture of Experts (MoE)\n\n> code a 2-expert router layer; route tokens dynamically\n> plot expert utilization histograms over dataset\n> simulate sparse/dense swaps; measure FLOP savings\n\n Grouped Query Attention\n\n> convert your mini-former to grouped query layout\n> measure speed vs vanilla multi-head on large batch\n> ablate number of groups, plot latency\n\n Normalization & 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for million-token demos\n- interleaved pipelines: +10% throughput, almost free\n- MoE: top-1 routing + expert-choice/hierarchical shuffling = practical at scale\n\nkeep this map in mind: always ask; what’s your bottleneck?\n- weights, work, or state? \n- the right parallelism makes the big thing smaller,\n- without making communication the new bottleneck\n\n- you made it to the end\n- now you know: every parallelism axis is a lever, not a panacea\n- the real move is combining axes for your exact bottleneck; fit, throughput, context, or tail latency\n- next time you see “we train at 8M tokens/sec on 1024 GPUs,” read between the lines: it’s all about how they split the problem\n\ntl;dr: llms are simple, just brutal at scale\n- hardware-aware optimization is survival\n- the real skill is matching bottleneck to the right parallelism; otherwise you’re just moving the 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":32,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":96,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":457,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":1596,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for million-token demos\n- interleaved pipelines: +10% throughput, almost free\n- MoE: top-1 routing + expert-choice/hierarchical shuffling = practical at scale\n\nkeep this map in mind: always ask; what’s your bottleneck?\n- weights, work, or state? \n- the right parallelism makes the big thing smaller,\n- without making communication the new bottleneck\n\n- you made it to the end\n- now you know: every parallelism axis is a lever, not a panacea\n- the real move is combining axes for your exact bottleneck; fit, throughput, context, or tail latency\n- next time you see “we train at 8M tokens/sec on 1024 GPUs,” read between the lines: it’s all about how they split the problem\n\ntl;dr: llms are simple, just brutal at scale\n- hardware-aware optimization is survival\n- the real skill is matching bottleneck to the right parallelism; otherwise you’re just moving the 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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append it, repeat, each new token becomes part of the sequence for the next prediction\n\n- models are more than weight files\n- neural network architecture: transformer skeleton (layers, heads, RoPE, MQA/GQA, more below)\n- weights: billions of learned numbers (parameters, not \"tokens\", but calculated from tokens)\n- tokenizer: how text gets chunked into tokens (BPE/SentencePiece)\n- config: metadata, shapes, special tokens, license, intended use, etc\n- sometimes: chat template are required for chat/instruct models, or else you get gibberish\n- you give a model a prompt (your text, converted into tokens)\n\n- models differ in parameter size:\n- 7B means ~7 billion learned numbers\n- common sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,55],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/4RyEp1mbjp","expanded_url":"https://x.com/TheAhmadOsman/status/1979651813793226993/photo/1","id_str":"1979651811943288832","indices":[56,79],"media_key":"3_1979651811943288832","media_url_https":"https://pbs.twimg.com/media/G3ki1ZXWkAAHioG.jpg","type":"photo","url":"https://t.co/4RyEp1mbjp","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"medium":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"small":{"faces":[{"x":225,"y":96,"h":154,"w":154}]},"orig":{"faces":[{"x":225,"y":96,"h":154,"w":154}]}},"sizes":{"large":{"h":627,"w":656,"resize":"fit"},"medium":{"h":627,"w":656,"resize":"fit"},"small":{"h":627,"w":656,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":627,"width":656,"focus_rects":[{"x":0,"y":260,"w":656,"h":367},{"x":29,"y":0,"w":627,"h":627},{"x":106,"y":0,"w":550,"h":627},{"x":342,"y":0,"w":314,"h":627},{"x":0,"y":0,"w":656,"h":627}]},"media_results":{"result":{"media_key":"3_1979651811943288832"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1979651813793226993","view_count":119484,"bookmark_count":401,"created_at":1760820747000,"favorite_count":2420,"quote_count":11,"reply_count":21,"retweet_count":70,"user_id_str":"248951926","conversation_id_str":"1979651813793226993","full_text":"new favorite meme dropped\n\nwill quote rt papers with it https://t.co/4RyEp1mbjp","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,164],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1059589378224218112","name":"James Nguyen","screen_name":"JamesNguyen868","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"JamesNguyen868","lang":"en","retweeted":false,"scopes":{"followers":false},"fact_check":null,"id":"1979422743025086949","view_count":158,"bookmark_count":1,"created_at":1760766132000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979408446534398403","full_text":"@JamesNguyen868 RTX PRO is 96GB\n\nThat 128GB of VRAM, in inference, is a bottleneck\n\nIt's the reason why Llama 3.1 70B took 13 minutes to finish processing 4k tokens","in_reply_to_user_id_str":"1059589378224218112","in_reply_to_status_id_str":"1979421741391990882","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,72],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1408089187","name":"Devon Bray","screen_name":"esologic","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"esologic","lang":"en","retweeted":false,"fact_check":null,"id":"1979601835314012474","view_count":58,"bookmark_count":0,"created_at":1760808831000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979579611458535622","full_text":"@esologic Have you done any benchmarking? Would love to see some numbers","in_reply_to_user_id_str":"1408089187","in_reply_to_status_id_str":"1979579611458535622","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,152],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1975917353071517765?t=Yz15omvxJD1NGDSKleWoNw&s=19","url":"https://t.co/udhUiwUTPq","indices":[129,152]}],"user_mentions":[{"id_str":"855269061495635968","name":"Val 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month\n\nhttps://t.co/udhUiwUTPq","in_reply_to_user_id_str":"855269061495635968","in_reply_to_status_id_str":"1979642090465509856","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[11,112],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1493067396415954945","name":"FP32 Monastic","screen_name":"kv_cached","indices":[0,10]}]},"favorited":false,"in_reply_to_screen_name":"kv_cached","lang":"en","retweeted":false,"fact_check":null,"id":"1979627080909508779","view_count":435,"bookmark_count":1,"created_at":1760814850000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1979597401435283751","full_text":"@kv_cached good point, i know that exllama supports it as well\n\nit's best used for coding w/ greedy sampling btw","in_reply_to_user_id_str":"1493067396415954945","in_reply_to_status_id_str":"1979622877768089737","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-10-20","value":49599,"startTime":1760832000000,"endTime":1760918400000,"tweets":[{"bookmarked":false,"display_text_range":[0,288],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1979965928566612003","view_count":6610,"bookmark_count":21,"created_at":1760895638000,"favorite_count":149,"quote_count":2,"reply_count":20,"retweet_count":8,"user_id_str":"248951926","conversation_id_str":"1979965928566612003","full_text":"my timeline is full of GPUs\n\n> everyone talking about GPUs\n> everyone asking about GPUs\n> everyone acquiring GPUs\n> even Mac Studio truthers… Buying GPUs\n> even the ones who mocked me… Buying GPUs\n\nour movement is winning\nthis is the good timeline\n\n—Buy a GPU, The 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All Time\n> beat out SBF, Elizabeth Holmes, and your favorite VC\n> literally built an empire out of copy-pasted Amazon Mechanical Turk tasks\n\n> mfw I labeled 4chan posts for pennies and turned it into a 14B exit\n> mfw I am now leading Meta's quest for godlike AI\n> mfw data quality was never part of the business model\n> never bet against the grind","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,292],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1981058326738747501","view_count":39210,"bookmark_count":1431,"created_at":1761156086000,"favorite_count":804,"quote_count":3,"reply_count":15,"retweet_count":69,"user_id_str":"248951926","conversation_id_str":"1981058326738747501","full_text":"> be you\n> want to actually learn how LLMs work\n> sick of “just start with linear algebra and come back in 5 years”\n> decide to build my own roadmap\n> no fluff. no detours. no 200-hour generic ML playlists\n> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”\n\n> goal: build, fine-tune, and ship LLMs\n> not vibe with them. not \"learn the theory\" forever\n> build them\n\n> you will:\n\n> > build an autograd engine from scratch\n> > write a mini-GPT from scratch\n> > implement LoRA and fine-tune a model on real data\n> > hate CUDA at least once\n> > cry\n> > keep going\n\n> 5 phases\n> if you already know something? skip\n> if you're lost? rewatch\n> if you’re stuck? use DeepResearch\n> this is a roadmap, not a leash\n> by the end: you either built the thing or you didn’t\n\n> phase 0: foundations\n\n> > if matrix multiplication is scary, you’re not ready yet\n> > watch 3Blue1Brown’s linear algebra series\n> > MIT 18.06 with Strang, yes, he’s still the GOAT\n> > code Micrograd from scratch (Karpathy)\n> > train a mini-MLP on MNIST\n> > no frameworks, no shortcuts, no mercy\n\n> phase 1: transformers\n\n> > the name is scary\n> > it’s just stacked matrix multiplies and attention blocks\n> > Jay Alammar + 3Blue1Brown for the “aha”\n> > Stanford CS224N for the theory\n> > read \"Attention Is All You Need\" only AFTER building mental models\n> > Karpathy's \"Let's Build GPT\" will break your brain in a good way\n> > project: build a decoder-only GPT from scratch\n> > bonus: swap tokenizers, try BPE/SentencePiece\n\n> phase 2: scaling\n\n> > LLMs got good by scaling, not magic\n> > Kaplan paper -> Chinchilla paper\n> > learn Data, Tensor, Pipeline parallelism\n> > spin up multi-GPU jobs using HuggingFace Accelerate\n> > run into VRAM issues\n> > fix them\n> > welcome to real training hell\n\n> phase 3: alignment & fine-tuning\n\n> > RLHF: OpenAI blog -> Ouyang paper\n> > SFT -> reward model -> PPO (don’t get lost here)\n> > Anthropic's Constitutional AI = smart constraints\n> > LoRA/QLoRA: read, implement, inject into HuggingFace models\n> > fine-tune on real data\n> > project: fine-tune gpt2 or distilbert with your own adapters\n> > not toy examples. real use cases or bust\n\n> phase 4: production\n\n> this is the part people skip to, but you earned it\n> inference optimization: FlashAttention, quantization, sub-second latency\n> read the paper, test with quantized models\n\n> resources:\n\n> math/coding:\n> > 3Blue1Brown, MIT 18.06, Goodfellow’s book\n\n> PyTorch:\n> > Karpathy, Zero to Mastery\n> > transformers:\n> > Alammar, Karpathy, CS224N, Vaswani et al\n> > scaling:\n> > Kaplan, Chinchilla, HuggingFace Accelerate\n> > alignment:\n> > OpenAI, Anthropic, LoRA, QLoRA\n> > inference:\n> > FlashAttention\n\n> the endgame:\n\n> > understand how these models actually work\n> > see through hype\n> > ignore LinkedIn noise\n> > build tooling\n> > train real stuff\n> > ship your own stack\n> > look at a paper and think “yeah I get it”\n> > build 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supervised fine-tuning (sft)\n> use high-quality, well-structured chat data and\n> pick a solid template for consistent turns\n\n> sft gives you a stable, cost-effective baseline\n> don’t skip it, even if you plan to go deeper\n\n> next, optimize for user preferences\n> direct preference optimization (dpo),\n> or its variants like kernelized (kto),\n> online (orpo), or adversarial (apo)\n> these methods actually teach the model\n> what “better” looks like beyond simple mimicry\n\n> once you’ve got preference alignment,go on-policy:\n> reinforcement learning from human feedback (rlhf)\n> or on-policy distillation, which lets your model learn\n> from real interactions or stronger models\n> this is how you get reliability and sharper behaviors\n\n> the post-training pipeline is where\n> assistants are truly sculpted;\n> skipping steps means leaving performance,\n> safety, and steerability on the table\n\ninfra is the boss fight\n\n> this is where most teams lose time,\n> money, and sanity 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sizes: 7B, 13B, 70B\n- bigger = stronger, but eats more VRAM/memory & compute\n- the model computes a probability for every possible next token (softmax over vocab)\n- picks one: either the highest (greedy) or\n- samples from the probability distribution (temperature, top-p, etc)\n- then appends that token to the sequence, then repeats the whole process\n- this is generation:\n- generate; predict, sample, append\n- over and over, one token at a time\n- rinse and repeat\n- each new token depends on everything before it; the model re-reads the sequence every step\n\n- generation is always stepwise: token by token, not all at once\n- mathematically: model is a learned function, f_θ(seq) → p(next_token)\n- all the \"magic\" is just repeating \"what's likely next?\" until you stop\n\n- all conversation \"tokens\" live in the KV cache, or the \"session memory\"\n\n- so what's actually inside the model?\n- everything above-tokens, weights, config-is just setup for the real engine underneath\n\n- the core of almost every modern llm is a transformer architecture\n- this is the skeleton that moves all those numbers around\n- it's what turns token sequences and weights into predictions\n- designed for sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,129],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/cjqAHAcRvY","expanded_url":"https://x.com/TheAhmadOsman/status/1984734990907637904/photo/1","id_str":"1984734989401849856","indices":[130,153],"media_key":"3_1984734989401849856","media_url_https":"https://pbs.twimg.com/media/G4sx9RCWAAACyx_.jpg","type":"photo","url":"https://t.co/cjqAHAcRvY","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":400,"w":400,"resize":"fit"},"medium":{"h":400,"w":400,"resize":"fit"},"small":{"h":400,"w":400,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":400,"width":400,"focus_rects":[{"x":0,"y":58,"w":400,"h":224},{"x":0,"y":0,"w":400,"h":400},{"x":49,"y":0,"w":351,"h":400},{"x":130,"y":0,"w":200,"h":400},{"x":0,"y":0,"w":400,"h":400}]},"media_results":{"result":{"media_key":"3_1984734989401849856"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984734990907637904","view_count":2382,"bookmark_count":4,"created_at":1762032671000,"favorite_count":44,"quote_count":0,"reply_count":9,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984734990907637904","full_text":"i fear not the man who has practiced 10,000 punches once, but i fear the man who has practiced one punch 10,000 times\n\n— gilfoyle https://t.co/cjqAHAcRvY","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,153],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","url":"https://t.co/ATDeto3e85","indices":[130,153]}],"user_mentions":[]},"favorited":false,"in_reply_to_screen_name":"TheAhmadOsman","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/ATDeto3e85","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=IYYUNee2-KyUtZwKQor7jQ&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984481381326455151","view_count":89847,"bookmark_count":69,"created_at":1761972205000,"favorite_count":200,"quote_count":0,"reply_count":13,"retweet_count":5,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"may i interest you in some of my GPUs while youʼre here?\n\np.s. scroll through my profile for local AI, GPUs, and hardware guides\n\nhttps://t.co/ATDeto3e85","in_reply_to_user_id_str":"248951926","in_reply_to_status_id_str":"1984413196242075728","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,55],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2672119496","name":"Abod 🚀","screen_name":"abod_ftw","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"abod_ftw","lang":"en","retweeted":false,"fact_check":null,"id":"1984481655923294393","view_count":30939,"bookmark_count":5,"created_at":1761972271000,"favorite_count":75,"quote_count":0,"reply_count":3,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@abod_ftw 100%\n\nthatʼs why i said “Buy a GPU,” singular","in_reply_to_user_id_str":"2672119496","in_reply_to_status_id_str":"1984481393657655692","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,134],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"2990914369","name":"🏴☠️ krys (bun)","screen_name":"NinjaBunny9000","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"NinjaBunny9000","lang":"en","retweeted":false,"fact_check":null,"id":"1984493278402883790","view_count":40340,"bookmark_count":20,"created_at":1761975042000,"favorite_count":216,"quote_count":0,"reply_count":29,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@NinjaBunny9000 an RTX 3060 for $200 is a good starting point\n\nyou do not go from 0 to 100, in fact you shouldnʼt go from a 0 to a 100","in_reply_to_user_id_str":"2990914369","in_reply_to_status_id_str":"1984492691262939302","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,131],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","url":"https://t.co/XbXOPaWyRw","indices":[108,131]}],"user_mentions":[{"id_str":"17150012","name":"Socialtrainers","screen_name":"Socialtrainers","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Socialtrainers","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980027070085513232","quoted_status_permalink":{"url":"https://t.co/XbXOPaWyRw","expanded":"https://x.com/TheAhmadOsman/status/1980027070085513232?t=ypcZXH-qhsteIfz_h-8OIA&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1984483094540533875","view_count":23330,"bookmark_count":27,"created_at":1761972614000,"favorite_count":22,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@Socialtrainers check this thread out (and my pinned tweet)\n\nposted in preparation for the website launch \n\nhttps://t.co/XbXOPaWyRw","in_reply_to_user_id_str":"17150012","in_reply_to_status_id_str":"1984482634375053605","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,98],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/serving-a…","expanded_url":"https://ahmadosman.com/blog/serving-ai-from-the-basement-part-i/","url":"https://t.co/R3pVxfuFDv","indices":[75,98]}],"user_mentions":[{"id_str":"1722965472738439168","name":"James Watkins","screen_name":"DoozerDiffuser","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"DoozerDiffuser","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1984563472227611064","view_count":27654,"bookmark_count":27,"created_at":1761991777000,"favorite_count":32,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984413196242075728","full_text":"@DoozerDiffuser more than you think :)\n\ncheck my profile as well for more\n\nhttps://t.co/R3pVxfuFDv","in_reply_to_user_id_str":"1722965472738439168","in_reply_to_status_id_str":"1984563064478273620","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,15],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"433539857","name":"ctlst","screen_name":"radmadvlad","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"radmadvlad","lang":"qme","retweeted":false,"fact_check":null,"id":"1984514572410114250","view_count":287,"bookmark_count":0,"created_at":1761980119000,"favorite_count":3,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@radmadvlad 💯💯💯","in_reply_to_user_id_str":"433539857","in_reply_to_status_id_str":"1984511575836016942","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[12,22],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1822607547527839744","name":"Kenshii 💢","screen_name":"kenshii_ai","indices":[0,11]}]},"favorited":false,"in_reply_to_screen_name":"kenshii_ai","lang":"en","retweeted":false,"fact_check":null,"id":"1984509008921739362","view_count":266,"bookmark_count":0,"created_at":1761978792000,"favorite_count":4,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984497376145318214","full_text":"@kenshii_ai Thanks man","in_reply_to_user_id_str":"1822607547527839744","in_reply_to_status_id_str":"1984508410088354231","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[13,35],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"70378340","name":"Daniel Bellone","screen_name":"danibellone","indices":[0,12]}]},"favorited":false,"in_reply_to_screen_name":"danibellone","lang":"en","retweeted":false,"fact_check":null,"id":"1984475981789139246","view_count":132,"bookmark_count":0,"created_at":1761970918000,"favorite_count":0,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984157512795357614","full_text":"@danibellone You're very welcome :)","in_reply_to_user_id_str":"70378340","in_reply_to_status_id_str":"1984475500681838664","is_quote_status":0,"is_ai":null,"ai_score":null}]},{"label":"2025-11-03","value":115915,"startTime":1762041600000,"endTime":1762128000000,"tweets":[{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","quoted_status_id_str":"1984595329971318856","quoted_status_permalink":{"url":"https://t.co/eqiWU7TrFM","expanded":"https://twitter.com/rohanpaul_ai/status/1984595329971318856","display":"x.com/rohanpaul_ai/s…"},"retweeted":false,"fact_check":null,"id":"1984781249412481342","view_count":189,"bookmark_count":0,"created_at":1762043699000,"favorite_count":4,"quote_count":1,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1984781249412481342","full_text":"this guy is malicious, opensource FTW\n\na reminder that\n- not your Weights means eventually not the same Model btw\n\nthey can\n- quantize it\n- distill it\n- hot-swap to a cheaper/weaker checkpoint\n- make the model manipulative\n- fine-tune it in ways that break safety or depth\n- drop its IQ\n- run experiments on you and/or your data\n- throttle output speed or raise prices\n- sunset the entire model/version\n\nthey have all the knobs & you're at their mercy\n\nyou won't even get a changelog\n\nopensource FTW","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,293],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1984791510324314388","view_count":37341,"bookmark_count":958,"created_at":1762046146000,"favorite_count":603,"quote_count":3,"reply_count":15,"retweet_count":66,"user_id_str":"248951926","conversation_id_str":"1984791510324314388","full_text":"step-by-step LLM Engineering Projects\n\neach project = one concept learned the hard (i.e. real) way\n\n Tokenization & Embeddings\n\n> build byte-pair encoder + train your own subword vocab\n> write a “token visualizer” to map words/chunks to IDs\n> one-hot vs learned-embedding: plot cosine distances\n\n Positional Embeddings\n\n> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four\n> animate a toy sequence being “position-encoded” in 3D\n> ablate positions—watch attention collapse\n\n Self-Attention & Multihead Attention\n\n> hand-wire dot-product attention for one token\n> scale to multi-head, plot per-head weight heatmaps\n> mask out future tokens, verify causal property\n\n transformers, QKV, & stacking\n\n> stack the Attention implementations with LayerNorm and residuals → single-block transformer\n> generalize: n-block “mini-former” on toy data\n> dissect Q, K, V: swap them, break them, see what explodes\n\n Sampling Parameters: temp/top-k/top-p\n\n> code a sampler dashboard — 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coders, secure your systems with this tool in 1 easy step https://t.co/qu0a8Z9KhU","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,280],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1986066887759757543","view_count":26680,"bookmark_count":460,"created_at":1762350220000,"favorite_count":344,"quote_count":5,"reply_count":18,"retweet_count":37,"user_id_str":"248951926","conversation_id_str":"1986066887759757543","full_text":"- you are\n- a person neck-deep in LLMs, wading through a sea of jargon\n- “shards,” “experts,” “layers across nodes,” “tensor parallelism,”\n- “pipeline parallelism,” “data parallelism”\n- you keep hearing epic claims like\n - “8 million tokens/sec on 1024 GPUs”\n - and wonder, what the hell does that even look like on real hardware?\n- we'll start with what “layers” even are,\n- then walk the five parallelism axes,\n- and then show how they combine\n\n- is every GPU running a full copy of the model?\n- or are the weights sharded across the cluster?\n- are layers sliced up? what are “layers” to begin with?\n - a “layer” = attention + MLP + residual glue\n - ≈ a transformer block; then we stack 24 to 160 of them\n - more on that below\n- do whole blocks migrate to different nodes?\n- does the batch get carved into pieces?\n- is the context length split? or both?\n- when do all these devices need to chat with each other?\n- and what are they sending?\n- what’s an “expert” in a Mixture-of-Experts model?\n- and why do people make it sound like air-traffic control for tokens?\n\n- as LLMs got bigger, nothing fit on one GPU\n- so parallelism became the only way out\n- but the word “parallelism” hides a zoo of strategies\n- each one optimized for a different kind of problem\n\n- sometimes you duplicate the model for raw throughput\n - data parallelism\n- sometimes you break each giant matrix multiply across devices\n - tensor/model parallelism\n- sometimes you hand off entire layers to the next GPU\n - pipeline parallelism\n- sometimes you dispatch tokens to “experts” scattered all over\n - expert parallelism\n- and sometimes, when the context is massive\n - you even split the sequence itself\n\ninference vs training? whole different beast\n- inference:\n - cares about context length\n - key/value cache (KV cache) size\n - serving tons of requests with minimal idle time\n - prefill vs decode become separate bottlenecks\n- training:\n - wants big batches\n - needs fast gradient sync\n - huge memory for activations\n - relentless all-reduce ops\n\nso, when you see those monster numbers and wild claims, ask:\n- what’s split?\n- what’s duplicated?\n- what’s just being pipelined through?\n- who’s doing all the talking?\n - GPUs?\n - CPUs?\n - or the people writing the framework hacks?\n\nparallelism 101 isn’t just for LLM nerds\n- it’s survival for anyone who wants to\n - train\n - fine-tune\n - or serve\n- models bigger than a single graphics card\n- and we’re gonna break it all down, once and for all\n\nfirst, a quick recap of how LLMs work\n- llms are giant next-token guessers\n- feed in tokens, model returns probabilities for what comes next\n- this is conditional modeling: p(x_{t+1} | x_{1:t}); all \"reasoning\" is just maximizing next-token prediction over long contexts\n- parallelism pressure: high accuracy means bigger models, longer contexts; both shatter single-device limits\n\n- inference: just a loop\n- sample → append → feed back in; repeat until done\n- really two stages: prefill (encode the prompt, attention-heavy) and decode (one token at a time, cache-heavy)\n- parallelism pressure: prefill wants maximum compute in parallel; decode needs memory bandwidth and batching\n\n- training: minimizes how wrong those guesses are\n- cross-entropy loss between predicted and actual token\n- scale up data, parameters, context → better loss, but optimizer states and gradients balloon memory usage\n- parallelism pressure: gradient sync and optimizer memory dominate; communication overhead becomes a first-class problem\n\n- inside the model: a tall stack of transformer blocks\n- each processes a sequence of vectors (tokens → embeddings)\n- all blocks edit a shared “residual stream”\n- parallelism hint: deep? pipeline split; wide? tensor split\n\n- each block does two things\n- self-attention: mix info across tokens (global context)\n- mlp: rewrite each token’s vector (local transform)\n- this global-then-local alternates for dozens to hundreds of layers\n- parallelism hint: attention is sequence-bottlenecked, MLPs are flop-bottlenecked; each wants its own split\n\n- layernorm + residuals keep things stable\n- helps gradients flow; most use pre-norm\n- deep stacks become trainable, but activations stay huge\n- parallelism hint: activation memory = batch × seq × hidden; target for sequence/context sharding and activation checkpointing\n\n- self-attention = queries, keys, values\n- dot-products score “who attends to whom”; softmax makes weights; multi-heads see different relationships\n- compute is O(L²) during prefill, O(L) per token in decode\n- parallelism hint: quadratic prefill drives context parallelism; big KV drives KV sharding\n\n- to scale: GQA / MQA (grouped/multi-query attention)\n- fewer key/value sets per head, saves bandwidth/memory\n- tiny quality hit, huge efficiency win (at ≤128 k context; when tuned)\n- smaller KV → more requests/longer contexts per GPU\n - rough rule: every extra 65k tokens at 70B costs ~2 GB/GPU even with GQA—yes, that adds up fast\n- parallelism hint: makes tensor/data parallel more efficient with fixed memory\n\n- position info: added or rotated\n- learned embeddings or rotary encodings (RoPE)\n- lets models generalize to longer sequences; RoPE directly encodes relative position in attention\n- parallelism hint: long contexts make context/sequence parallelism indispensable\n\n- mlp is the heavy lifter\n- expand → activate → project down\n- MoE = many mlps, only a few active per token; scale params without scaling compute, but adds network overhead\n- dense MLPs dominate FLOPs; MoE swaps dense compute for routing and all-to-all comms\n- parallelism hint: dense → tensor/pipeline, MoE → expert parallel + token shuffling\n\n- top of the model: one last layernorm, then lm head\n- maps hidden states to vocab logits; often reuses input embedding weights\n- weight tying helps parameter and cache efficiency\n- parallelism hint: LM head is another big matmul; follows tensor/pipeline split of previous layers\n\n- decoding: pick a sampler\n- greedy, temp, top-k/p, beam, speculative; just ways to choose the next token\n- speculative/batch schedulers keep GPUs busy\n- parallelism hint: data parallel absorbs throughput, tensor/context sharding fights latency for long prompts\n\n- alignment (rlhf, dpo, etc.): guide preferences\n- tweaks outputs, core compute is unchanged\n- fine-tunes shift sampling tendencies, not compute graph\n- parallelism hint: training still needs gradient sync, same memory/comm bottlenecks\n\n- systems view: 95% is big matrix multiplies (gemms)\n- rest is cheap elementwise ops\n- hardware lives or dies by gemm performance\n- memory bandwidth, interconnect, and collectives (all-reduce/all-to-all) are the real ceilings\n- parallelism hint: pick the axis that minimizes your dominant comms pattern\n\n- two compute phases:\n- prefill: quadratic attention, load whole prompt\n- decode: one token at a time, kv cache is bottleneck\n- kv cache per layer ≈ (B × L × 2 × n_kv × d_head × bytes); multiply by layers = why memory vanishes\n- parallelism hint: prefill likes context/sequence sharding, decode likes batching and kv sharding\n\n- why split the model at all? simple concept, ugly in practice:\n - VRAM runs out\n - bandwidth bottlenecks\n - FLOPS max out\n- only way out: parallelism\n- weights, work, and state are each huge; but for different reasons\n - and each one pushes you toward a different split axis\n\nparallelism 101\n- single GPU:\n - can’t hold the weights\n - can’t fit the KV cache\n - doesn’t have enough compute\n- doesn’t scale to real LLMs\n- goal:\n - break up the compute\n - split the memory\n - keep every GPU doing something useful\n - avoid idle cycles and bottlenecks\n\ntensor parallelism (TP)\n- step up: model is too big, so shard within layers (split big matmuls/attn heads across GPUs)\n- for inference:\n - single prompt can use all TP devices; shard weights and KV cache across GPUs\n - unlocks longer context/one-shot mega-prompts (because each GPU only needs its chunk)\n - minimal latency penalty; comms are per-token and per-layer, but hidden in GEMM (if intra-node NVLink)\n - first thing you do when the model won’t fit in one GPU but you want max single-query performance\n- for training:\n - you split GEMMs (e.g., QKV/MLP) across N devices; everyone computes a slice, then you all-reduce at the end of the layer\n - comms: ~2 all-reduce ops per layer forward/backward\n - all math, no waiting for whole layers to finish\n - works best when all GPUs on fast local links (NVLink/NVSwitch), otherwise, you’re bottlenecked\n - pair with “sequence parallelism” (SP): shard along sequence axis for even bigger speedups/memory wins\n- rule: TP is your go-to for splitting big layers/weights/KV; always keep it intra-node if you can\n\npipeline parallelism (PP)\n- think “assembly line”: split the model by layers into stages, put each stage on a GPU (or group)\n- for inference:\n - each token passes through the pipeline (latency = num_stages × per-stage time)\n - adds overhead for low concurrency (pipeline bubbles)\n - but: lets you split very tall models across more GPUs (and multiple nodes)\n - often combined with TP (per stage) + DP (across nodes)\n- for training:\n - feed microbatches in like cars on a factory line\n - early GPUs start on batch N+1 while late GPUs are still finishing batch N\n - scheduling: “1F1B” (one-forward-one-backward) is classic, interleaved is better (reduces bubbles, keeps more cards hot)\n - comms: point-to-point; just send activations/gradients to the next/prev stage\n - fills/drains pipeline at start/end, so latency is worse for small batches\n- rule: use PP when model depth > what fits per device/group; throughput wins, single-token latency suffers\n\ndata parallelism (DP)\n- start simple: every GPU/node gets a full copy of the model\n- for inference: each replica can serve requests independently; easy throughput scaling, zero comms\n - but: each replica needs its own KV cache; you burn VRAM linearly with batch count\n- for training: each works on a different chunk of the data, then they sync gradients (all-reduce)\n - memory: each replica has full weights, grads, optimizer state\n - comms: one big all-reduce per step (bottleneck: slow interconnect or huge models)\n - modern fix: ZeRO/FSDP; shard optimizer states, grads, even params across devices\n - ZeRO-3: sharded everything; now any one GPU sees a fraction of the model/optimizer\n - lets you squeeze 100B+ params onto commodity hardware\n- rule: DP is how you get more throughput; for bigger models, combine with model parallelism\n\nexpert parallelism (EP, MoE)\n- Mixture of Experts (MoE): only some parameters get used per token; rest sleep, save FLOPs\n- for inference:\n - tokens routed to experts (ideally local); all-to-all overhead dominates small batch serving unless you get smart with locality/routing\n - top-1 routing + local experts is the new meta; keeps network from killing tail latency\n - MoE good for “big brain, small budget”; huge capacity, modest compute, but only if you nail the routing and don’t bottleneck network\n- for training:\n - “experts” (sub-MLPs) spread across GPUs; router sends each token to top-1/2/4 experts\n - comms: two big all-to-all shuffles per MoE layer (tokens to experts, then back)\n - modern tricks: Expert-Choice, dropless routing, hierarchical shuffles; less network pain, better load balance\n - only the selected experts do work, so you scale parameter count without scaling compute linearly\n - parallelize: TED = Tensor × Expert × Data (often add Pipeline for 4D grid)\n- rule: use EP to unlock parameter scaling beyond dense; just mind the network, and tune routing for your hardware\n\nsequence/context parallelism (SP/CP)\n- the bonus round: split the sequence axis, not just layers or weights\n - why: LLMs with 128k, 1M, or more context tokens = “KV cache and attention matrices are the new bottleneck”\n- for inference/training:\n - shard activations/KV cache across sequence positions (instead of model dims or layers)\n - Megatron Context Parallelism, DeepSpeed-Ulysses, Ring-Attention: new tricks for linear scaling with context size\n - pairs well with TP/PP/EP, especially for long-context, high-concurrency serving\n- rule: add SP/CP when context/KV cache eats your VRAM, or you want fast prefill for million-token prompts\n\nputting it together: real-world layouts\n- serving LLMs (inference)\n - model fits on one GPU? just use DP, batch requests, done\n - model too big? add TP (intra-node, 2-4 GPUs, shard weights/KV)\n - still too big? PP (split layers), then DP for multi-node throughput\n - long contexts? CP/SP (shard sequence/KV), paged attention, GQA/MQA to shrink KV\n - MoE? use EP, but always fight for local experts and top-1 routing\n- training LLMs\n - start with DP (and ZeRO/FSDP) for throughput/memory relief\n - model too wide? add TP\n - model too deep? add PP, interleave for less pipeline bubble\n - MoE? grid up: TED (+ PP), tune batch/expert per device for best balance\n - context too big? 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sequence axis\n- fixes long-context prefill and kv memory by splitting tokens/devices;\n- pairs well with tp and paged kv caches\n\nsystems cheat sheet: when to use what\n- DP: for throughput and data sharding; ZeRO/FSDP to cut memory; inference just replicas\n- TP: for single-model multi-GPU (shard big layers, weights, KV); keep within fast local links\n- PP: for tall models (split by layer); more stages = more bubble, but necessary for true giants\n- EP (MoE): for max params at fixed compute; tune routing and batch; network is the limiter\n- SP/CP: for ultra-long context or when KV cache dominates; essential for 128k+ or high concurrency\n- Combine: 3D, 4D, even 5D grids (e.g., TP×EP×DP×PP×SP) for trillion-param monsters\n\nmodern tricks\n- paged attention, continuous batching (vLLM, TensorRT-LLM): unlock huge batch + context + utilization\n- GQA/MQA: shrink KV cache and bandwidth, scale to long prompts\n- context parallelism: finally scales prefill to dozens of GPUs for 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few things\n\n- still posting here, not going anywhere\n- but X isn’t indexable, so old threads basically get lost\n- i either have to re-post stuff (and get yelled at by some), or tell people “good luck finding that tweet from june”\n\ngot a couple dms about subscription pricing\n\n- massive thanks to everyone who’s subbed to help with keeping the GPUs on and the electric bill paid up haha,\n- but honestly, most if not all content will always end up free\n- there are bonus perks as a thank you for anyone choosing to support\n- and sometimes paid users might see posts early for feedback or polish\n\n- but to be clear, SUPPORTING IS COMPLETELY OPTIONAL\n- if you want to join the GPU Vanguard, that’s its own thing hahaha\n- you absolutely don’t need to pay a cent\n\n- the plan is for the guides and breakdowns to land on learnllmsdotai at some point\n- not rushing into another deadline though, i’ve got a lot on my plate right now\n- just wanted to kick off actually organizing all this content, and to do it properly without stressing over a massive launch or deadline\n\n- tl;dr\n- content stays FREE, support is OPTIONAL (comes with some bonus perks), and everything will get easier to find over time\n\nfinally, thanks for sticking around through the chaos, the alpha drops, the memes, and the re-posts; it’s been fun, and hopefully only gets better from here","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[14,59],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1910178797766213632","name":"wAIve.online","screen_name":"wAIve_online","indices":[0,13]}]},"favorited":false,"in_reply_to_screen_name":"wAIve_online","lang":"en","retweeted":false,"fact_check":null,"id":"1985886198775366087","view_count":1054,"bookmark_count":0,"created_at":1762307140000,"favorite_count":1,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@wAIve_online that's a llama.cpp issue, use exllama or vllm","in_reply_to_user_id_str":"1910178797766213632","in_reply_to_status_id_str":"1985885882205786614","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[7,159],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"ahmadosman.com/blog/do-not-us…","expanded_url":"https://ahmadosman.com/blog/do-not-use-llama-cpp-or-ollama-on-multi-gpus-setups-use-vllm-or-exllamav2/","url":"https://t.co/RYu0Zou752","indices":[136,159]}],"user_mentions":[{"id_str":"19914039","name":"iDare e/acc","screen_name":"idare","indices":[0,6]}]},"favorited":false,"in_reply_to_screen_name":"idare","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1985961539325591931","view_count":332,"bookmark_count":1,"created_at":1762325103000,"favorite_count":4,"quote_count":0,"reply_count":0,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985751771638350061","full_text":"@idare for multi-GPU builds, yes, if you have to do partial CPU offloading then nothing beats llama.cpp\n\narticle below touches on that\n\nhttps://t.co/RYu0Zou752","in_reply_to_user_id_str":"19914039","in_reply_to_status_id_str":"1985901793042448828","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[9,68],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1401204486","name":"Harrison Kinsley","screen_name":"Sentdex","indices":[0,8]}]},"favorited":false,"in_reply_to_screen_name":"Sentdex","lang":"en","retweeted":false,"fact_check":null,"id":"1986052008613126591","view_count":1069,"bookmark_count":0,"created_at":1762346672000,"favorite_count":6,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1985810689458569502","full_text":"@Sentdex China is gonna win this round, huh... Very bearish 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Alexandr\n> unbothered\n> moisturized\n> thriving https://t.co/qrVoDKGBdQ","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,279],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"favorited":false,"lang":"en","retweeted":false,"fact_check":null,"id":"1988638203495022998","view_count":10751,"bookmark_count":256,"created_at":1762963269000,"favorite_count":264,"quote_count":4,"reply_count":12,"retweet_count":26,"user_id_str":"248951926","conversation_id_str":"1988638203495022998","full_text":"- local llms 101\n\n- running a model = inference (using model weights)\n- inference = predicting the next token based on your input plus all tokens generated so far\n- together, these make up the \"sequence\"\n\n- tokens ≠ words\n- they're the chunks representing the text a model sees\n- they are represented by integers (token IDs) in the model\n- 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sequence data (like language),\n- transformers can \"look back\" at previous tokens and\n- decide which ones matter for the next prediction\n\n- transformers work in layers, passing your sequence through the same recipe over and over\n- each layer refines the representation, using attention to focus on the important parts of your input and context\n- every time you generate a new token, it goes through this stack of layers-every single step\n\n- inside each transformer layer:\n- self-attention: figures out which previous tokens are important to the current prediction\n- MLPs (multi-layer perceptrons): further process token representations, adding non-linearity and expressiveness\n- layer norms and residuals: stabilize learning and prediction, making deep networks possible\n- positional encodings (like RoPE): tell the model where each token sits in the sequence\n- so \"cat\" and \"catastrophe\" aren't confused by position\n\n- by stacking these layers (sometimes dozens or even hundreds)\n- transformers build a complex understanding of your prompt, context, and conversation history\n\n- transformer recap:\n- decoder-only: model only predicts what comes next, each token looks back at all previous tokens\n- self-attention picks what to focus on (MQA/GQA = efficient versions for less memory)\n- feed-forward MLP after attention for every token (usually 2 layers, GELU activation)\n- everything's wrapped in layer norms + linear layers (QKV projections, MLPs, outputs)\n- residuals + norms = stable, trainable, no exploding/vanishing gradients\n- RoPE (rotary embeddings): tells the model where each token sits in the sequence\n- stack N layers of this → final logits → pick the next token\n- scale up: more layers, more heads, wider MLPs = bigger brains\n\n- VRAM: memory, the bottleneck\n- VRAM must must fit:\n1. weights (main model, whether quantized or not)\n2. KV cache (per token, per layer, per head)\n- weights:\n- FP16: ~2 bytes/param → 7B = ~14GB\n- 8-bit: ~1 byte/param → 7B = ~7GB\n- 4-bit: ~0.5 byte/param → 7B = ~3.5GB\n- add 10–30% for runtime overheads\n- KV cache:\n- rule of thumb: 0.5MB per token (Llama-like 7B, 32 layers, 4K tokens = ~2GB)\n- some runtimes support KV cache quantization (8/4-bit) = big savings\n\n- throughput = memory bandwidth + GPU FLOPs + attention implementation (FlashAttention/SDPA help) + quantization + batch size\n- offload to CPU? expect MASSIVE slowdown\n\n- GPU or bust: CPUs run quantized models (slow), but any real context/model needs CUDA/ROCm/Metal\n- CPU spill = sadness (check device_map and memory fit)\n\n- quantization: reduce precision for memory wins (sometimes a tiny quality hit)\n- FP32/FP16/BF16 = full/floored\n- INT8/INT4/NF4 = quantized\n- 4-bit (NF4/GPTQ/AWQ) = sweet spot for most consumer GPUs (big memory win, small quality hit for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, coding)\n\n- KV cache quantization: even more memory saved for long contexts (check runtime support)\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU/portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced flavors for special hardware/use\n- protip: avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = speed/memory, but maybe less accurate\n- local = more control/knobs, more work\n\n- what happens when you \"load a model\"?\n- download weights, tokenizer, config\n- resolve license/trust (don't use trust_remote_code unless you really trust the author)\n- load to VRAM/CPU (check memory fit)\n- warmup: kernels/caches initialized, first pass is slowest\n- inference: forward passes per token, updating KV cache each step\n\n- decoding = how next token is chosen:\n- greedy: always top-1 (robotic)\n- temperature: softens or sharpens probabilities (higher = more random)\n- top-k: pick from top k\n- top-p: pick from smallest set with ≥p prob\n- typical sampling, repetition penalty, no-repeat n-gram: extra controls\n- deterministic = set a seed and no sampling\n- tune for your use-case: chat, summarization, code\n\n- serving options?\n- vLLM for high throughput, parallel serving\n- llama.cpp server (OpenAI-compatible API)\n- ExLlama V2/V3 w/ Tabby API (OpenAI-compatible API)\n- run as a local script (CLI)\n- FastAPI/Flask for local API endpoint\n\n- local ≠ offline; run it, serve it, or build apps on top\n\n- fine-tuning, ultra-brief:\n- LoRA / QLoRA = adapter layers (efficient, minimal VRAM)\n- still need a dataset and eval plan; adapters can be merged or kept separate\n- most users get far with prompting + retrieval (RAG) or few-shot for niche tasks\n\n- common pitfalls\n- OOM? out of memory. Model or context too big, quantize or shrink context\n- gibberish? used a base model with a chat prompt, or wrong template; check temperature/top_p\n- slow? offload to CPU, wrong drivers, no FlashAttention; check CUDA/ROCm/Metal, memory fit\n- unsafe? don't use random .bin or trust_remote_code; prefer safetensors, verify source\n\n- why run locally?\n- control: all the knobs are yours to tweak:\n- sampler, chat templates, decoding, system prompts, quantization, context\n- cost: no per-token API billing-just upfront hardware\n- privacy: prompts and outputs stay on your machine\n- latency: no network roundtrips, instant token streaming\n\n- challenges:\n- hardware limits (VRAM/memory = max model/context)\n- ecosystem variance (different runtimes, quant schemes, templates)\n- ops burden (setup, drivers, updates)\n\n- running local checklist:\n- pick a model (prefer chat-tuned, sized for your VRAM)\n- pick precision (4-bit saves RAM, FP16 for max quality)\n- install runtime (vLLM, llama.cpp, Transformers+PyTorch, etc)\n- run it, get tokens/sec, check memory fit\n- use correct chat template (apply_chat_template)\n- tune decoding (temp/top_p)\n- benchmark on your task\n- serve as local API (or go wild and fine-tune it)\n\n- glossary:\n- token: smallest unit (subword/char)\n- context window: max tokens visible to model\n- KV cache: session memory, per-layer attention state\n- quantization: lower precision for memory/speed\n- RoPE: rotary position embeddings (for order)\n- GQA/MQA: efficient attention for memory bandwidth\n- decoding: method for picking next token\n- RAG: retrieval-augmented generation, add real info\n\n- misc:\n- common architectures: LLaMA, Falcon, Mistral, GPT-NeoX, etc\n- base model: not fine-tuned for chat (LLaMA, Falcon, etc)\n- chat-tuned: fine-tuned for dialogue (Alpaca, Vicuna, etc)\n- instruct-tuned: fine-tuned for following instructions (LLaMA-2-Chat, Mistral-Instruct, etc)\n\n- chat/instruct models usually need a special prompt template to work well\n- chat template: system/user/assistant markup is required; wrong template = junk output\n- base models can do few-shot chat prompting, but not as well as chat-tuned ones\n\n- quantized: weights stored in lower precision (8-bit, 4-bit) for memory savings, at some quality loss\n- quantization is a tradeoff: memory/speed vs quality\n- 4-bit (NF4/GPTQ/AWQ) is the sweet spot for most consumer GPUs (huge memory win, minor quality drop for most tasks)\n- math-heavy or finicky tasks degrade first (math, logic, code)\n- quantization types: FP16 (full), INT8 (quantized), INT4/NF4 (more quantized), etc.\n- some runtimes support quantized KV cache (8/4-bit), big savings for long contexts\n\n- formats/runtimes:\n- PyTorch + safetensors: flexible, standard, works on GPU/TPU/CPU\n- GGUF (llama.cpp): CPU/GPU, portable, best for quant + edge devices\n- ONNX, TensorRT-LLM, MLC: advanced options for special hardware\n\n- avoid legacy .bin (pickle risk), use safetensors for safety\n\n- everything is a tradeoff:\n- smaller = fits anywhere, less power\n- more context = more latency + VRAM burn\n- quantization = faster/leaner, maybe less accurate\n- local = full control/knobs, but more work\n\n- final words:\n- local LLMs = memory math + correct formatting\n- fit weights and KV cache in memory\n- use the right chat template and decoding strategy\n- know your knobs: quantization, context, decoding, batch, hardware\n\n- master these, and you can run (and reason about) almost any modern model locally","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[0,284],"entities":{"hashtags":[],"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[]},"extended_entities":{"media":[{"display_url":"pic.x.com/1HsfEtZKGI","expanded_url":"https://x.com/TheAhmadOsman/status/1988657796926083399/photo/1","id_str":"1988657261544136704","indices":[285,308],"media_key":"3_1988657261544136704","media_url_https":"https://pbs.twimg.com/media/G5khPjZW4AAdGlb.jpg","type":"photo","url":"https://t.co/1HsfEtZKGI","ext_media_availability":{"status":"Available"},"features":{"large":{"faces":[]},"medium":{"faces":[]},"small":{"faces":[]},"orig":{"faces":[]}},"sizes":{"large":{"h":1001,"w":1440,"resize":"fit"},"medium":{"h":834,"w":1200,"resize":"fit"},"small":{"h":473,"w":680,"resize":"fit"},"thumb":{"h":150,"w":150,"resize":"crop"}},"original_info":{"height":1001,"width":1440,"focus_rects":[{"x":0,"y":0,"w":1440,"h":806},{"x":0,"y":0,"w":1001,"h":1001},{"x":0,"y":0,"w":878,"h":1001},{"x":146,"y":0,"w":501,"h":1001},{"x":0,"y":0,"w":1440,"h":1001}]},"media_results":{"result":{"media_key":"3_1988657261544136704"}}}]},"favorited":false,"lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"retweeted":false,"fact_check":null,"id":"1988657796926083399","view_count":45653,"bookmark_count":831,"created_at":1762967940000,"favorite_count":796,"quote_count":1,"reply_count":7,"retweet_count":73,"user_id_str":"248951926","conversation_id_str":"1988657796926083399","full_text":"last month, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs \n\nin this project, you’ll build all the essentials, all under 8k lines of code\n\n> train the tokenizer, new rust implementation\n> pretrain a transformer LLM on fineweb \n> evaluate core score across a bunch of metrics\n\n> midtrain, user-assistant convos from smoltalk, \n> multiple choice Qs, tool use\n\n> sft, then eval the chat model on:\n> world knowledge MCQ (arc-e/c, mmlu)\n> math (gsm8k)\n> code (humaneval)\n\n> rl the model (optionally) on gsm8k with “grpo”\n\n> efficient inference:\n> kv cache, fast prefill/decode\n> tool use (python interpreter, sandboxed)\n> access via cli or chatgpt-like webui\n\n> write a single markdown report card, \n> summarizing + gamifying the whole pipeline\n\nthe model you’ll build:\n\n> rotary only (no positional embeddings)\n> qk norm\n> untied embedding / unembedding\n> norm after token embedding\n> relu² mlp\n> no biases in linears\n> rmsnorm (no learnable params)\n> mqa (multi-query attention)\n> logit softcap\n> optimizer: muon + adamw\n\nif i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes\n\nstart now and you'll be glad you did in a few weeks :)","in_reply_to_user_id_str":null,"in_reply_to_status_id_str":null,"is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,168],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1821393366652973056","name":"Martin_DeVido","screen_name":"d33v33d0","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"d33v33d0","lang":"en","retweeted":false,"fact_check":null,"id":"1988456293946851422","view_count":5977,"bookmark_count":1,"created_at":1762919898000,"favorite_count":13,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@d33v33d0 who thought that starting with that would lead to anything but some X engagement?\n\nit's like they know this app isn't gonna make it so let's rage bait with it","in_reply_to_user_id_str":"1821393366652973056","in_reply_to_status_id_str":"1988443013471973478","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,139],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"812722131263979521","name":"Daniel Habibi","screen_name":"catslikeseaweed","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"catslikeseaweed","lang":"en","retweeted":false,"fact_check":null,"id":"1988452743476916301","view_count":6010,"bookmark_count":0,"created_at":1762919052000,"favorite_count":103,"quote_count":0,"reply_count":2,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988435385840451983","full_text":"@catslikeseaweed i am not kidding, some founders i know would do 10x easy with that kind of money they just aren't grifters enough for this","in_reply_to_user_id_str":"812722131263979521","in_reply_to_status_id_str":"1988442612236156954","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[17,141],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"109175215","name":"exquisitegeist","screen_name":"ghost_exquisite","indices":[0,16]}]},"favorited":false,"in_reply_to_screen_name":"ghost_exquisite","lang":"en","retweeted":false,"fact_check":null,"id":"1988511760815431797","view_count":2393,"bookmark_count":0,"created_at":1762933123000,"favorite_count":33,"quote_count":0,"reply_count":4,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@ghost_exquisite you're completely undermining the capabilities of LLMs and how much of a productivity boosters they could be when done right","in_reply_to_user_id_str":"109175215","in_reply_to_status_id_str":"1988511202012516808","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[16,85],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1980026689217298545","url":"https://t.co/oGAtLnFZ7x","indices":[62,85]}],"user_mentions":[{"id_str":"1622573135391510530","name":"Mejalism_Records","screen_name":"Occult_Aircuts","indices":[0,15]}]},"favorited":false,"in_reply_to_screen_name":"Occult_Aircuts","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1980026689217298545","quoted_status_permalink":{"url":"https://t.co/oGAtLnFZ7x","expanded":"https://x.com/TheAhmadOsman/status/1980026689217298545","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988516284569461023","view_count":3938,"bookmark_count":23,"created_at":1762934201000,"favorite_count":14,"quote_count":0,"reply_count":1,"retweet_count":2,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Occult_Aircuts budget? use case? have you read this thread?\n\nhttps://t.co/oGAtLnFZ7x","in_reply_to_user_id_str":"1622573135391510530","in_reply_to_status_id_str":"1988516034064576586","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,249],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","url":"https://t.co/NKR8gZ7EAw","indices":[226,249]}],"user_mentions":[{"id_str":"1369348853414178822","name":"Alex Finn","screen_name":"AlexFinn","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"AlexFinn","lang":"en","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1937209604552741152","quoted_status_permalink":{"url":"https://t.co/NKR8gZ7EAw","expanded":"https://x.com/TheAhmadOsman/status/1937209604552741152?t=XSC_5Dw6zIBvDUb33DvGHg&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988648338271662446","view_count":6031,"bookmark_count":147,"created_at":1762965685000,"favorite_count":65,"quote_count":0,"reply_count":4,"retweet_count":6,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@AlexFinn Hey Alex, if LLMs are your main goal, I would recommend this roadmap I posted back in June. Also, my last tweet goes through the core LLM concepts that everyone should know, I suggest you take a look at it as well.\n\nhttps://t.co/NKR8gZ7EAw","in_reply_to_user_id_str":"1369348853414178822","in_reply_to_status_id_str":"1988639124954456081","is_quote_status":1,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[8,30],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[],"user_mentions":[{"id_str":"1147761409419333632","name":"Divanshu Chauhan","screen_name":"Divkix","indices":[0,7]}]},"favorited":false,"in_reply_to_screen_name":"Divkix","lang":"cs","retweeted":false,"fact_check":null,"id":"1988629432236409031","view_count":2044,"bookmark_count":0,"created_at":1762961178000,"favorite_count":5,"quote_count":0,"reply_count":1,"retweet_count":0,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@Divkix definitely a GPU setup","in_reply_to_user_id_str":"1147761409419333632","in_reply_to_status_id_str":"1988629158344200339","is_quote_status":0,"is_ai":null,"ai_score":null},{"bookmarked":false,"display_text_range":[10,33],"entities":{"hashtags":[],"symbols":[],"timestamps":[],"urls":[{"display_url":"x.com/TheAhmadOsman/…","expanded_url":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","url":"https://t.co/5hZs49NWRh","indices":[10,33]}],"user_mentions":[{"id_str":"1544398698217558018","name":"GoX ✝️ 🇺🇸🇮🇱🇹🇼🇯🇵🇺🇦","screen_name":"EagleGoX","indices":[0,9]}]},"favorited":false,"in_reply_to_screen_name":"EagleGoX","lang":"qme","possibly_sensitive":false,"possibly_sensitive_editable":true,"quoted_status_id_str":"1964869801404420396","quoted_status_permalink":{"url":"https://t.co/5hZs49NWRh","expanded":"https://x.com/TheAhmadOsman/status/1964869801404420396?t=0NWl83lULcIn5Xm93hxm_w&s=19","display":"x.com/TheAhmadOsman/…"},"retweeted":false,"fact_check":null,"id":"1988637458326487497","view_count":1646,"bookmark_count":0,"created_at":1762963091000,"favorite_count":10,"quote_count":0,"reply_count":1,"retweet_count":1,"user_id_str":"248951926","conversation_id_str":"1988510607084048794","full_text":"@EagleGoX 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