What is llama.cpp used for?

llama.cpp is a dependency-free C/C++ engine for running large language models locally on CPUs and GPUs, with quantization, a CLI and an HTTP server. It is a good fit for running LLMs on laptops and desktops, embedding inference in C/C++ applications, and serving local models over HTTP.

Is llama.cpp open source?

Yes. llama.cpp is open source under the MIT licence. Its source code is on GitHub at ggml-org/llama.cpp and is written mainly in C++.

Yes. llama.cpp is open source, so the software itself is free to use.

Can I self-host llama.cpp?

Yes. llama.cpp can be self-hosted on your own server or infrastructure; there is no official hosted version.

What is llama.cpp an alternative to?

llama.cpp is an open-source alternative to OpenAI API Platform, Mistral AI, Google AI Studio and xAI API. Other open-source alternatives to OpenAI API Platform include Ollama, LocalAI and Hugging Face Chat UI.

Is llama.cpp actively maintained?

Yes. The most recent commit to llama.cpp was on 13 October 2026, and the latest release is v0.6.0, published on 5 October 2026. The project has 131k stars on GitHub.

llama.cpp: open-source alternative to OpenAI API Platform

About llama.cpp

llama.cpp is an open-source library and set of tools for running large language model inference, including vision-language models, with minimal setup. The aim is strong performance across many kinds of hardware, whether local or in the cloud. It is a plain C/C++ implementation without external dependencies, built on the ggml library, and is licensed under MIT.

The project optimizes for many platforms: Apple silicon through ARM NEON, Accelerate and Metal, x86 with AVX, AVX2, AVX512 and AMX, and several RISC-V extensions. Quantization from 1.5-bit to 8-bit integers reduces memory use and speeds up inference. GPU backends include CUDA for NVIDIA, HIP for AMD, MUSA, Vulkan and SYCL, and hybrid CPU plus GPU inference lets you partly accelerate models larger than available VRAM.

It provides a command-line interface and llama-server, which offers a REST API and a built-in web UI. You can install it from pre-built binaries, Docker or by building from source. Other local-AI tools such as Ollama build on it, which makes it relevant to developers embedding inference in their own applications and hobbyists running models on modest hardware.

Key features

Dependency-free C/C++ inference engine
Quantization from 1.5-bit to 8-bit
CUDA, HIP, Metal, Vulkan and SYCL backends
CPU plus GPU hybrid inference
llama-server with REST API and web UI
Optimized for Apple silicon and x86

Good fit for

Running LLMs on laptops and desktops
Embedding inference in C/C++ applications
Serving local models over HTTP

Built with: C++ tools
Tags: llm; inference; cpp; quantization; local-ai; ggml; gpu; self-hosted

llama.cpp: questions and answers

What is llama.cpp used for?: llama.cpp is a dependency-free C/C++ engine for running large language models locally on CPUs and GPUs, with quantization, a CLI and an HTTP server. It is a good fit for running LLMs on laptops and desktops, embedding inference in C/C++ applications, and serving local models over HTTP.
Is llama.cpp open source?: Yes. llama.cpp is open source under the MIT licence. Its source code is on GitHub at ggml-org/llama.cpp and is written mainly in C++.
Is llama.cpp free?: Yes. llama.cpp is open source, so the software itself is free to use.
Can I self-host llama.cpp?: Yes. llama.cpp can be self-hosted on your own server or infrastructure; there is no official hosted version.
What is llama.cpp an alternative to?: llama.cpp is an open-source alternative to OpenAI API Platform, Mistral AI, Google AI Studio and xAI API. Other open-source alternatives to OpenAI API Platform include Ollama, LocalAI and Hugging Face Chat UI.
Is llama.cpp actively maintained?: Yes. The most recent commit to llama.cpp was on 13 October 2026, and the latest release is v0.6.0, published on 5 October 2026. The project has 131k stars on GitHub.