llm.c vs nanochat

llm.c

• Developers who want to see how a GPT-2 style model is trained at the lowest level
• Students learning how neural network layers are implemented in C or CUDA
• People with an NVIDIA GPU who want to reproduce GPT-2 (124M) training
• Readers who want a small reference implementation to compare against PyTorch

nanochat

• Students who want to see how a full LLM training pipeline fits together
• Developers who want a readable, modifiable LLM training codebase
• Researchers running small experiments on compute-optimal model sizes
• People with access to a multi-GPU cloud node who want a reproducible GPT-2-scale training run

llm.c

• Code is small and direct, making it useful for studying training internals
• The README reports llm.c running a bit faster than PyTorch Nightly (about 7%) in its own benchmark
• Includes a PyTorch reference implementation and unit tests to check correctness

nanochat

• Covers the full LLM pipeline in one codebase, according to the README
• A single --depth setting automatically sets the other hyperparameters
• Includes a public leaderboard that tracks the wall-clock time to reach GPT-2-level capability

llm.c

• People who want a ready-to-use chatbot or inference app, since the README focuses on training
• Users without a GPU who want practical training speed, because the README says CPU training will not go far
• Teams that need Python-based ecosystem tooling, since the README says the repo only maintains C and CUDA code

nanochat

• People who want a polished chatbot to use day to day: the README says the speedrun model is like talking to a kindergartener
• People without GPU access who expect strong results: the CPU/MPS path shrinks the model and the README says results will be weak
• Anyone needing production-grade support: questions are routed to DeepWiki, Discussions, and Discord rather than a formal support channel

llm.c

• Requires NVIDIA CUDA hardware for practical training; CPU use is described as a demo
• Setup can be involved: multi-GPU and cuDNN paths need MPI, NCCL, and cuDNN installed
• The README is truncated and the repo has no releases or topics, so some details are unclear

nanochat

• Needs substantial GPU hardware for the main workflow: the README describes an 8x H100 node, and GPUs with under 80GB may need tuning
• The README says the CPU and Apple Silicon path produces weak results and the author has not tested all non-CUDA backends
• Cost figures are estimates tied to provider pricing at the time of writing and depend on your cloud setup