rmonsurate/llama.cpp

LLM inference in C/C++

C++

8,929 commits

updated Sep 30, 2026

llama.app

See the code

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Source	Message	Score	Date
	Releasing two post quantized trained models with fine tuned draft heads: Victoria (REAP + QAD) 44% smaller Qwen Flash Next, 70% TB 2.0 avg of three runs & Maple: Victoria + Canandian dataset finetune (r/LocalLLaMA) We had a Dell B300 in the lab for a few weeks and used it to create two fine tunes of Qwen Flash Next. The goal was the reduce the model in size as much as possible, while keeping the engram untouched on disk, to get the fastest, smartest, agentic model possible with a large amount of world…	0	Sep 30, 2026

README

llama.cpp

LLM inference in C/C++

ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API

Quick start

A few options to get llama.cpp installed on your machine:

Visit https://llama.app and follow the instructions
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:

# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF

# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

VLM session with llama cli

Built-in web UI against llama serve

Description

The main goal of llama.cpp is to enable LLM (and VLM) 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 build on top of the ggml library.

Supported backends

Backend	Target devices
BLAS	All
BLIS	All
CANN	Ascend NPU
CUDA	Nvidia GPU
HIP	AMD GPU
Hexagon [In Progress]	Snapdragon
IBM zDNN	IBM Z & LinuxONE
MUSA	Moore Threads GPU
Metal	Apple Silicon
OpenCL	Adreno GPU
OpenVINO [In Progress]	Intel CPUs, GPUs, and NPUs
RPC	All
SYCL	Intel GPU
VirtGPU	VirtGPU APIR
Vulkan	GPU
WebGPU	All
ZenDNN	AMD CPU

Documentation

Tools

Development

Contributing

Contributors can open PRs
Collaborators will be invited based on contributions
Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
Any help with managing issues, PRs and projects is very appreciated!
Read the CONTRIBUTING.md for more information

Acknowledgements

yhirose/cpp-httplib - Single-header HTTP server, used by llama-server - MIT license
nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain

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Languages

C++

56.0%

15.6%

Python

7.3%

Cuda

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TypeScript

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Svelte

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rmonsurate/llama.cpp

LLM inference in C/C++

C++

8,929 commits

updated Sep 30, 2026

llama.app

See the code

See what people are saying

Newest first

Most points

Source	Message	Score	Date
	Releasing two post quantized trained models with fine tuned draft heads: Victoria (REAP + QAD) 44% smaller Qwen Flash Next, 70% TB 2.0 avg of three runs & Maple: Victoria + Canandian dataset finetune (r/LocalLLaMA) We had a Dell B300 in the lab for a few weeks and used it to create two fine tunes of Qwen Flash Next. The goal was the reduce the model in size as much as possible, while keeping the engram untouched on disk, to get the fastest, smartest, agentic model possible with a large amount of world…	0	Sep 30, 2026

README

llama.cpp

LLM inference in C/C++

ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API

Quick start

A few options to get llama.cpp installed on your machine:

Visit https://llama.app and follow the instructions
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:

# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF

# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

VLM session with llama cli

Built-in web UI against llama serve

Description

The main goal of llama.cpp is to enable LLM (and VLM) 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 build on top of the ggml library.

Supported backends

Backend	Target devices
BLAS	All
BLIS	All
CANN	Ascend NPU
CUDA	Nvidia GPU
HIP	AMD GPU
Hexagon [In Progress]	Snapdragon
IBM zDNN	IBM Z & LinuxONE
MUSA	Moore Threads GPU
Metal	Apple Silicon
OpenCL	Adreno GPU
OpenVINO [In Progress]	Intel CPUs, GPUs, and NPUs
RPC	All
SYCL	Intel GPU
VirtGPU	VirtGPU APIR
Vulkan	GPU
WebGPU	All
ZenDNN	AMD CPU

Documentation

Tools

Development

Contributing

Contributors can open PRs
Collaborators will be invited based on contributions
Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
Any help with managing issues, PRs and projects is very appreciated!
Read the CONTRIBUTING.md for more information

Acknowledgements

yhirose/cpp-httplib - Single-header HTTP server, used by llama-server - MIT license
nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain

Similar repos

MBZUAI-IFM/llama.cpp

LLM inference in C/C++

vagrillo/llama.cpp

LLM inference in C/C++

unslothai/llama.cpp

LLM inference in C/C++

229

jadidbourbaki/llama.cpp

LLM inference in C/C++

Veridian-Zenith/llama.cpp

ggml-org/llama.cpp

LLM inference in C/C++

129,974

ggerganov/llama.cpp

LLM inference in C/C++

129,925

gaetan-puleo/llama-cpp-strix-halo

Languages

C++

56.0%

15.6%

Python

7.3%

Cuda

5.4%

TypeScript

4.3%

Svelte

2.3%

HTML

2.1%

Metal

1.5%

Jinja

1.2%