llama.cpp vs llama-cpp-python

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

llama-cpp-python

Open source · MIT

Python bindings that let you run llama.cpp language models from Python code or a local OpenAI-style server.

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 llama-cpp-python if you need…

Python developers who want to run a language model locally from their own code
Developers who want a local server that mimics the OpenAI API for existing tools
Users building LangChain or LlamaIndex apps who want a local model backend
People who want to run models on specific hardware such as NVIDIA, AMD, Apple Metal, or Vulkan GPUs using the documented build options

Side-by-side

	llama.cpp	llama-cpp-python
Type	Library	Library
Price	Free and open source	Free and open source
Licence	MIT	MIT
GitHub stars	133k	10.7k
Setup difficulty	Intermediate	Intermediate
How you get it	See the README	Install with a package manager
Main language	C++	Python
Last code update	4 days ago	36 days 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.

llama-cpp-python

This package wraps llama.cpp, a program for running large language models on your own computer, so Python code can load a model and generate text with it. It offers a high-level Python API, compatibility with LangChain and LlamaIndex, and an OpenAI-compatible web server you can point other tools at. People use it to run models locally instead of calling a cloud service.

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

llama-cpp-python

Python developers who want to run a language model locally from their own code
Developers who want a local server that mimics the OpenAI API for existing tools
Users building LangChain or LlamaIndex apps who want a local model backend
People who want to run models on specific hardware such as NVIDIA, AMD, Apple Metal, or Vulkan GPUs using the documented build options

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

llama-cpp-python

Gives an OpenAI-style interface, so many existing tools can connect with little change
Supports several hardware backends, including CUDA, Metal, HIP, Vulkan, and SYCL, through build options
Released under the MIT license with active documentation and a test badge in the README

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

llama-cpp-python

People who only want a ready-made chat app; this is a library and server for developers, not a graphical application, based on the README
Users with no Python or compiler setup, since the default install builds llama.cpp from source and needs a C compiler
Anyone needing a guarantee of performance on their hardware; the README does not give benchmarks

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

llama-cpp-python

Installing from source requires a C compiler and CMake-based build, which can fail and needs troubleshooting
Pre-built wheels are listed as options for specific platforms and CUDA or ROCm versions, so availability depends on your setup
Performance and model support depend on the underlying llama.cpp project, which the README does not detail

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

llama-cpp-python

Install from PyPI (builds llama.cpp from source)

pip install llama-cpp-python

Install a pre-built CPU wheel

pip install llama-cpp-python \
  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu

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

Frequently asked questions

What is the difference between llama.cpp and llama-cpp-python?

llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. llama-cpp-python: Python bindings that let you run llama.cpp language models from Python code or a local OpenAI-style server. llama.cpp suits developers who want to run open-weight models locally without a cloud api, while llama-cpp-python suits python developers who want to run a language model locally from their own code.

Are llama.cpp and llama-cpp-python free?

llama.cpp: Free and open source. llama-cpp-python: Free and open source.

Is llama.cpp or llama-cpp-python more popular?

llama.cpp has more GitHub stars (133k) than llama-cpp-python (10.7k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to llama.cpp and llama-cpp-python?

Other options people compare them with include Ollama.

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.