cohere-ai/llama.cpp

cohere-ai/llama.cpp

Description: LLM inference in C/C++

License: MIT

Stars: 0

Forks: 0

Open issues: 8

Created: 2025-08-20T21:32:58Z

Pushed: 2026-02-24T19:39:05Z

Default branch: master

Fork: yes

Parent repository: ggml-org/llama.cpp

Archived: no

README:

llama.cpp

!llama


Manifesto / ggml / ops

LLM inference in C/C++

Recent API changes

• Changelog for `libllama` API
• Changelog for `llama-server` REST API

Hot topics

• [guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)
• [[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)
• Support for the gpt-oss model with native MXFP4 format has been added | PR | Collaboration with NVIDIA | Comment
• Hot PRs: All | Open
• Multimodal support arrived in llama-server: #12898 | [documentation](./docs/multimodal.md)
• VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
• Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
• Introducing GGUF-my-LoRA https://github.com/ggml-org/llama.cpp/discussions/10123
• Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669
• Hugging Face GGUF editor: discussion | tool

----

Quick start

Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:

• Install llama.cpp using [brew, nix or winget](docs/install.md)
• Run with Docker - see our [Docker documentation](docs/docker.md)
• Download pre-built binaries from the releases page
• Build from source by cloning this repository - check out [our build guide](docs/build.md)

Once installed, you'll need a model to work with. Head to the [Obtaining and quantizing models](#obtaining-and-quantizing-models) section to learn more.

Example command:

# Use a local model file
llama-cli -m my_model.gguf

# Or download and run a model directly from Hugging Face
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

# Launch OpenAI-compatible API server
llama-server -hf ggml-org/gemma-3-1b-it-GGUF

Description

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

• Plain C/C++ implementation without any dependencies
• Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
• AVX, AVX2, AVX512 and AMX support for x86 architectures
• 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
• Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
• Vulkan and SYCL backend support
• CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

The llama.cpp project is the main playground for developing new features for the ggml library.

Models

Typically finetunes of the base models below are supported as well.

Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md)

Text-only

• [X] LLaMA 🦙
• [x] LLaMA 2 🦙🦙
• [x] LLaMA 3 🦙🦙🦙
• [X] Mistral 7B
• [x] Mixtral MoE
• [x] DBRX
• [X] Falcon
• [X] Chinese LLaMA / Alpaca and Chinese LLaMA-2 / Alpaca-2
• [X] Vigogne (French)
• [X] BERT
• [X] Koala
• [X] Baichuan 1 & 2 + derivations
• [X] Aquila 1 & 2
• [X] Starcoder models
• [X] Refact
• [X] MPT
• [X] Bloom
• [x] Yi models
• [X] StableLM models
• [x] Deepseek models
• [x] Qwen models
• [x] PLaMo-13B
• [x] Phi models
• [x] PhiMoE
• [x] GPT-2
• [x] Orion 14B
• [x] InternLM2
• [x] CodeShell
• [x] Gemma
• [x] Mamba
• [x] Grok-1
• [x] Xverse
• [x] Command-R models…