llama.cpp

llama

Manifesto / ggml / ops

LLM inference in C/C++

Recent API changes

Hot topics

Hugging Face cache migration: models downloaded with -hf are now stored in the standard Hugging Face cache directory, enabling sharing with other HF tools.
guide : using the new WebUI of llama.cpp
guide : running gpt-oss with llama.cpp
[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗
Support for the gpt-oss model with native MXFP4 format has been added | PR | Collaboration with NVIDIA | Comment
Multimodal support arrived in llama-server: #12898 | documentation
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
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
Run with Docker - see our Docker documentation
Download pre-built binaries from the releases page
Build from source by cloning this repository - check out our build guide

Once installed, you'll need a model to work with. Head to the 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
RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V 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.

<details> <summary>Models</summary>

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

Instructions for adding support for new models: HOWTO-add-model.md

Text-only

[X] LLaMA 🦙
[x] LLaMA 2 🦙🦙
[x] LLaMA 3 🦙🦙🦙
[X] Mistral 7B
[x] Mixtral MoE
[x] DBRX
[x] Jamba
[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
[x] SEA-LION
[x] GritLM-7B + GritLM-8x7B
[x] OLMo
[x] OLMo 2
[x] OLMoE
[x] Granite models
[x] GPT-NeoX + Pythia
[x] Snowflake-Arctic MoE
[x] Smaug
[x] Poro 34B
[x] Bitnet b1.58 models
[x] Flan T5
[x] Open Elm models
[x] ChatGLM3-6b + ChatGLM4-9b + GLMEdge-1.5b + GLMEdge-4b
[x] GLM-4-0414
[x] SmolLM
[x] EXAONE-3.0-7.8B-Instruct
[x] FalconMamba Models
[x] Jais
[x] Bielik-11B-v2.3
[x] RWKV-7
[x] RWKV-6
[x] QRWKV-6
[x] GigaChat-20B-A3B
[X] Trillion-7B-preview
[x] Ling models
[x] LFM2 models
[x] Hunyuan models
[x] BailingMoeV2 (Ring/Ling 2.0) models

Multimodal

[x] LLaVA 1.5 models, LLaVA 1.6 models
[x] BakLLaVA
[x] Obsidian
[x] ShareGPT4V
[x] MobileVLM 1.7B/3B models
[x] Yi-VL
[x] Mini CPM
[x] Moondream
[x] [Bunny](https://github.com/

Llama.cpp

Install / Use

README