llama.cpp vs llama2.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.
Open source · MIT
Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs.
Open source · MIT
A small C program that runs small Llama 2 language models, with training done in PyTorch.
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 llama2.c if you need…
- Developers who want to see how LLM inference works in plain C
- Students learning the Llama 2 architecture end to end
- People experimenting with tiny models trained on narrow datasets like TinyStories
- Anyone who wants a minimal inference engine with no external dependencies
Side-by-side
| llama.cpp | llama2.c | |
|---|---|---|
| Type | Library | Other |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 133k | 20.2k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | See the README | Clone and build from source |
| Main language | C++ | C |
| Last code update | 4 days ago | 2 years 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.
llama2.c
This project runs Llama 2 style language models using a single short C file with no dependencies. The README says you train a model in PyTorch and then run it with that C program. It is mainly built for minimalism and learning how inference works, not for running large models quickly.
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
llama2.c
- Developers who want to see how LLM inference works in plain C
- Students learning the Llama 2 architecture end to end
- People experimenting with tiny models trained on narrow datasets like TinyStories
- Anyone who wants a minimal inference engine with no external dependencies
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
llama2.c
- Inference is a single short C file with no dependencies, which makes it easy to read
- The README includes a full train-plus-inference workflow in one repo
- Small models trained on narrow data can produce surprisingly coherent output, as shown in the examples
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
llama2.c
- People who need to run 7B or larger models, since the README says the code only does float32 inference and 13B+ does not currently work
- People who need fast production inference, since the README describes it as educational and float32-only
- People who want a polished, supported product, since the README says the project is young and moving quickly
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
llama2.c
- The README says only fp32 inference is supported, so large models need a lot of memory and run slowly
- 13B and larger models do not currently work according to the README
- Code Llama support is incomplete and the README calls it buggy
Getting started
llama.cpp
Install with curl (macOS/Linux installer from README)
curl -LsSf https://llama.app/install.sh | shInstall with PowerShell (Windows installer from README)
irm https://llama.app/install.ps1 | iexllama2.c
Clone the repository
git clone https://github.com/karpathy/llama2.c.gitOpen the repository folder
cd llama2.cFrequently asked questions
What is the difference between llama.cpp and llama2.c?
llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. llama2.c: A small C program that runs small Llama 2 language models, with training done in PyTorch. llama.cpp suits developers who want to run open-weight models locally without a cloud api, while llama2.c suits developers who want to see how llm inference works in plain c.
Are llama.cpp and llama2.c free?
llama.cpp: Free and open source. llama2.c: Free and open source.
Is llama.cpp or llama2.c more popular?
llama.cpp has more GitHub stars (133k) than llama2.c (20.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to llama.cpp and llama2.c?
Other options people compare them with include Ollama.
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.