llm.c vs minGPT
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A small C/CUDA codebase for training GPT-2-style language models without PyTorch.
Open source · MIT
A small, readable PyTorch re-implementation of GPT for training and running language models.
The short answer
Choose llm.c if you need…
- 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
Choose minGPT if you need…
- Students who want to read a working GPT implementation end to end
- Developers who want a small, hackable base for experimenting with transformer models
- People who want to load GPT-2 weights and generate text with PyTorch
- Researchers who prefer a minimal codebase over a large framework
Side-by-side
| llm.c | minGPT | |
|---|---|---|
| Type | Learning resource | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 31.4k | 25k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Cuda | Python |
| Last code update | 17 months ago | 2 years ago |
What each one is
llm.c
llm.c is a project by Andrej Karpathy that trains GPT-2 style language models using plain C and CUDA instead of a large framework like PyTorch. Its stated focus is pretraining, including reproducing the GPT-2 and GPT-3 model series. The code is meant to be readable, and the README says a CPU reference version fits in about 1,000 lines.
minGPT
minGPT is a compact PyTorch codebase that reimplements the GPT transformer model, covering both training and inference. The README describes it as roughly 300 lines of model code, split into a model file, a byte pair encoder, and a generic training loop, aimed at being easy to read and learn from. It is a library you import into your own Python project, with demos for tasks like addition and character-level text generation.
Best for
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
minGPT
- Students who want to read a working GPT implementation end to end
- Developers who want a small, hackable base for experimenting with transformer models
- People who want to load GPT-2 weights and generate text with PyTorch
- Researchers who prefer a minimal codebase over a large framework
Strengths
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
minGPT
- Small enough to read in an afternoon, with the model in about 300 lines according to the README
- Clear separation between model, tokenizer, and training loop
- MIT licensed and widely referenced in notebooks, blogs, and courses
Not ideal for
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
minGPT
- People who need an actively maintained project: the README says minGPT is in a semi-archived state and points to nanoGPT for newer work
- Anyone needing distributed training or mixed precision, which the README lists as todos
- Users looking for a ready-made chatbot or polished end-user app
Trade-offs
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
minGPT
- Semi-archived as of January 2023; the author suggests nanoGPT for newer development
- Test coverage is described as limited, and there is no requirements.txt yet per the README
- Lacks mixed precision and distributed training, which are listed as future todos
Getting started
llm.c
Clone the repository
git clone https://github.com/karpathy/llm.cDownload the starter pack (fp32 path)
chmod u+x ./dev/download_starter_pack.sh && ./dev/download_starter_pack.shminGPT
Clone the repository
git clone https://github.com/karpathy/minGPT.gitEnter the directory
cd minGPTFrequently asked questions
What is the difference between llm.c and minGPT?
llm.c: A small C/CUDA codebase for training GPT-2-style language models without PyTorch. minGPT: A small, readable PyTorch re-implementation of GPT for training and running language models. llm.c suits developers who want to see how a gpt-2 style model is trained at the lowest level, while minGPT suits students who want to read a working gpt implementation end to end.
Are llm.c and minGPT free?
llm.c: Free and open source. minGPT: Free and open source.
Which is easier to set up, llm.c or minGPT?
minGPT is easier to get started with (rated intermediate); llm.c is rated advanced and usually needs more setup.
Is llm.c or minGPT more popular?
llm.c has more GitHub stars (31.4k) than minGPT (25k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to llm.c and minGPT?
Other options people compare them with include llama.cpp.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.