[Jan 7 2026] nanochat miniseries v1 · karpathy/nanochat · Discussion #420
Why miniseries. The correct way to think about LLMs is that you are not optimizing for a single specific model but for a family models controlled by a single dial (the compute you wish to spend) to...
Why miniseries. The correct way to think about LLMs is that you are not optimizing for a single specific model but for a family models controlled by a single dial (the compute you wish to spend) to achieve monotonically better results. This allows you to do careful science of scaling laws and ultimately this is what gives you the confidence that when you pay for "the big run", the extrapolation will work and your money will be well spent. For the first public release of nanochat my focus was on end-to-end pipeline that runs the whole LLM pipeline with all of its stages. Now, I'm coming back…
saved by
related reading
- How To Scale Your Modeljax-ml.github.io
- GitHub - karpathy/nanochat: The best ChatGPT that $100 can buy.github.com
- Scaling Laws, Carefully | Lil'Loglilianweng.github.io
- Demystify Transformers: A Guide to Scaling Laws | by Yu-Cheng Tsai | Sage Ai | Mediummedium.com
- Reiner Pope – The math behind how LLMs are trained and serveddwarkesh.com
- Chinchillaarxiv.org
- Scaling: The State of Play in AIoneusefulthing.org
- IsoFLOP curves of large language models are extremely flatseverelytheoretical.wordpress.com
- GenAI Handbookgenai-handbook.github.io
- frontier model training methodologies | Alex Wa's Blogdjdumpling.github.io
- Bits, FLOPS, and Watts: A Systems-Level Perspective of Scaling LLMs — Part 1 | by Asheesh Goja | Mediummedium.com
- Getting Caught Up to Modern LLM Research | Samarth Goeldev.samarthgoel.com