llama.cpp vs llm.c

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.

llama.cpp

Open source · MIT

Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs.

Full review Website

llm.c

Open source · MIT

A small C/CUDA codebase for training GPT-2-style language models without PyTorch.

Full review Website

The short answer

Choose llama.cpp if you need…

Developers who want to run open-weight models locally without a cloud API
People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
Teams that want a self-hosted OpenAI-compatible endpoint
Researchers experimenting with quantized models on limited memory

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

Side-by-side

	llama.cpp	llm.c
Type	Library	Learning resource
Price	Free and open source	Free and open source
Licence	MIT	MIT
GitHub stars	133k	31.4k
Setup difficulty	Intermediate	Advanced
How you get it	See the README	Clone and build from source
Main language	C++	Cuda
Last code update	4 days ago	17 months ago

What each one is

llama.cpp

llama.cpp is an open-source project for running large language models (LLMs), and vision-language models, on your own hardware. It is written in C/C++ with no required dependencies and supports many chip types, including Apple silicon, x86 and RISC-V CPUs, and NVIDIA, AMD and other GPUs. People use it to run models locally or on their own servers, with a command-line tool, a built-in web UI, and an OpenAI-compatible API server.

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.

Best for

llama.cpp

Developers who want to run open-weight models locally without a cloud API
People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
Teams that want a self-hosted OpenAI-compatible endpoint
Researchers experimenting with quantized models on limited memory

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

Strengths

llama.cpp

Plain C/C++ with no required dependencies, and a wide range of supported hardware backends
Quantization options from 1.5-bit to 8-bit reduce memory needs
Large, active open-source project under the MIT license

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

Not ideal for

llama.cpp

Users who want a polished consumer chat app with no setup; the README points to command-line and build steps
People who need a managed cloud service, since this is software you run yourself
Users with very limited RAM or VRAM, since performance depends heavily on hardware and model size

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

Trade-offs

llama.cpp

Setup can require building from source or choosing the right backend for your hardware
Performance and supported features vary by backend and device, and some backends are marked in progress
Results depend on local hardware, so the README's performance claims are hard to verify for your machine

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

Getting started

llama.cpp

Install with curl (macOS/Linux installer from README)

curl -LsSf https://llama.app/install.sh | sh

Install with PowerShell (Windows installer from README)

irm https://llama.app/install.ps1 | iex

Step-by-step guide: how to use llama.cpp

llm.c

Clone the repository

git clone https://github.com/karpathy/llm.c

Download the starter pack (fp32 path)

chmod u+x ./dev/download_starter_pack.sh && ./dev/download_starter_pack.sh

Step-by-step guide: how to use llm.c

Frequently asked questions

What is the difference between llama.cpp and llm.c?

llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. llm.c: A small C/CUDA codebase for training GPT-2-style language models without PyTorch. llama.cpp suits developers who want to run open-weight models locally without a cloud api, while llm.c suits developers who want to see how a gpt-2 style model is trained at the lowest level.

Are llama.cpp and llm.c free?

llama.cpp: Free and open source. llm.c: Free and open source.

Which is easier to set up, llama.cpp or llm.c?

llama.cpp is easier to get started with (rated intermediate); llm.c is rated advanced and usually needs more setup.

Is llama.cpp or llm.c more popular?

llama.cpp has more GitHub stars (133k) than llm.c (31.4k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to llama.cpp and llm.c?

Other options people compare them with include Ollama, minGPT.

Keep comparing

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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.