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llama.cpp

Tue, 14 Oct 2025 15:29:21 +0800

ggml-org/llama.cpp

llama.cpp

Manifesto / ggml / ops

LLM inference in C/C++

Recent API changes

Hot topics

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
Hot PRs: All | Open
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
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
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
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

Text-only

LLaMA 🦙
LLaMA 2 🦙🦙
LLaMA 3 🦙🦙🦙
Mistral 7B
Mixtral MoE
DBRX
Falcon
Chinese LLaMA / Alpaca and Chinese LLaMA-2 / Alpaca-2
Vigogne (French)
BERT
Koala
Baichuan 1 & 2 + derivations
Aquila 1 & 2
Starcoder models
Refact
MPT
Bloom
Yi models
StableLM models
Deepseek models
Qwen models
PLaMo-13B
Phi models
PhiMoE
GPT-2
Orion 14B
InternLM2
CodeShell
Gemma
Mamba
Grok-1
Xverse
Command-R models
SEA-LION
GritLM-7B + GritLM-8x7B
OLMo
OLMo 2
OLMoE
Granite models
GPT-NeoX + Pythia
Snowflake-Arctic MoE
Smaug
Poro 34B
Bitnet b1.58 models
Flan T5
Open Elm models
ChatGLM3-6b + ChatGLM4-9b + GLMEdge-1.5b + GLMEdge-4b
GLM-4-0414
SmolLM
EXAONE-3.0-7.8B-Instruct
FalconMamba Models
Jais
Bielik-11B-v2.3
RWKV-6
QRWKV-6
GigaChat-20B-A3B
Trillion-7B-preview
Ling models
LFM2 models
Hunyuan models

Multimodal

Bindings

Python: ddh0/easy-llama
Python: abetlen/llama-cpp-python
Go: go-skynet/go-llama.cpp
Node.js: withcatai/node-llama-cpp
JS/TS (llama.cpp server client): lgrammel/modelfusion
JS/TS (Programmable Prompt Engine CLI): offline-ai/cli
JavaScript/Wasm (works in browser): tangledgroup/llama-cpp-wasm
Typescript/Wasm (nicer API, available on npm): ngxson/wllama
Ruby: yoshoku/llama_cpp.rb
Rust (more features): edgenai/llama_cpp-rs
Rust (nicer API): mdrokz/rust-llama.cpp
Rust (more direct bindings): utilityai/llama-cpp-rs
Rust (automated build from crates.io): ShelbyJenkins/llm_client
C#/.NET: SciSharp/LLamaSharp
C#/VB.NET (more features - community license): LM-Kit.NET
Scala 3: donderom/llm4s
Clojure: phronmophobic/llama.clj
React Native: mybigday/llama.rn
Java: kherud/java-llama.cpp
Java: QuasarByte/llama-cpp-jna
Zig: deins/llama.cpp.zig
Flutter/Dart: netdur/llama_cpp_dart
Flutter: xuegao-tzx/Fllama
PHP (API bindings and features built on top of llama.cpp): distantmagic/resonance (more info)
Guile Scheme: guile_llama_cpp
Swift srgtuszy/llama-cpp-swift
Swift ShenghaiWang/SwiftLlama
Delphi Embarcadero/llama-cpp-delphi

UIs

(to have a project listed here, it should clearly state that it depends on llama.cpp)

AI Sublime Text plugin (MIT)
cztomsik/ava (MIT)
Dot (GPL)
eva (MIT)
iohub/collama (Apache-2.0)
janhq/jan (AGPL)
johnbean393/Sidekick (MIT)
KanTV (Apache-2.0)
KodiBot (GPL)
llama.vim (MIT)
LARS (AGPL)
Llama Assistant (GPL)
LLMFarm (MIT)
LLMUnity (MIT)
LMStudio (proprietary)
LocalAI (MIT)
LostRuins/koboldcpp (AGPL)
MindMac (proprietary)
MindWorkAI/AI-Studio (FSL-1.1-MIT)
Mobile-Artificial-Intelligence/maid (MIT)
Mozilla-Ocho/llamafile (Apache-2.0)
nat/openplayground (MIT)
nomic-ai/gpt4all (MIT)
ollama/ollama (MIT)
oobabooga/text-generation-webui (AGPL)
PocketPal AI (MIT)
psugihara/FreeChat (MIT)
ptsochantaris/emeltal (MIT)
pythops/tenere (AGPL)
ramalama (MIT)
semperai/amica (MIT)
withcatai/catai (MIT)
Autopen (GPL)

Tools

akx/ggify – download PyTorch models from HuggingFace Hub and convert them to GGML
akx/ollama-dl – download models from the Ollama library to be used directly with llama.cpp
crashr/gppm – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage
Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)

Infrastructure

Paddler - Open-source LLMOps platform for hosting and scaling AI in your own infrastructure
GPUStack - Manage GPU clusters for running LLMs
llama_cpp_canister - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
llama-swap - transparent proxy that adds automatic model switching with llama-server
Kalavai - Crowdsource end to end LLM deployment at any scale
llmaz - ☸️ Easy, advanced inference platform for large language models on Kubernetes.

Games

Lucy’s Labyrinth - A simple maze game where agents controlled by an AI model will try to trick you.

Supported backends

Backend	Target devices
Metal	Apple Silicon
BLAS	All
BLIS	All
SYCL	Intel and Nvidia GPU
MUSA	Moore Threads GPU
CUDA	Nvidia GPU
HIP	AMD GPU
Vulkan	GPU
CANN	Ascend NPU
OpenCL	Adreno GPU
IBM zDNN	IBM Z & LinuxONE
WebGPU [In Progress]	All
RPC	All

Obtaining and quantizing models

The Hugging Face platform hosts a number of LLMs compatible with llama.cpp:

You can either manually download the GGUF file or directly use any llama.cpp-compatible models from Hugging Face or other model hosting sites, such as ModelScope, by using this CLI argument: -hf <user>/<model>[:quant]. For example:

`1`	`llama-cli -hf ggml-org/gemma-3-1b-it-GGUF`

By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable MODEL_ENDPOINT. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. MODEL_ENDPOINT=https://www.modelscope.cn/.

After downloading a model, use the CLI tools to run it locally - see below.

llama.cpp requires the model to be stored in the GGUF file format. Models in other data formats can be converted to GGUF using the convert_*.py Python scripts in this repo.

The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp:

Use the GGUF-my-repo space to convert to GGUF format and quantize model weights to smaller sizes
Use the GGUF-my-LoRA space to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
Use the GGUF-editor space to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
Use the Inference Endpoints to directly host llama.cpp in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)

To learn more about model quantization, read this documentation

`llama-cli`

A CLI tool for accessing and experimenting with most of `llama.cpp`’s functionality.

Run in conversation mode

Models with a built-in chat template will automatically activate conversation mode. If this doesn’t occur, you can manually enable it by adding -cnv and specifying a suitable chat template with --chat-template NAME

llama-cli -m model.gguf

# > hi, who are you?
# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
#
# > what is 1+1?
# Easy peasy! The answer to 1+1 is... 2!

Run in conversation mode with custom chat template

# use the "chatml" template (use -h to see the list of supported templates)
llama-cli -m model.gguf -cnv --chat-template chatml

# use a custom template
llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'

Run simple text completion

To disable conversation mode explicitly, use -no-cnv

1
2
3

llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv

# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.

Constrain the output with a custom grammar
1 2 3

llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/

`llama-server`

A lightweight, OpenAI API compatible, HTTP server for serving LLMs.

Start a local HTTP server with default configuration on port 8080

llama-server -m model.gguf --port 8080

# Basic web UI can be accessed via browser: http://localhost:8080
# Chat completion endpoint: http://localhost:8080/v1/chat/completions

Support multiple-users and parallel decoding

1
2

# up to 4 concurrent requests, each with 4096 max context
llama-server -m model.gguf -c 16384 -np 4

Enable speculative decoding

1
2

# the draft.gguf model should be a small variant of the target model.gguf
llama-server -m model.gguf -md draft.gguf

Serve an embedding model

1
2

# use the /embedding endpoint
llama-server -m model.gguf --embedding --pooling cls -ub 8192

Serve a reranking model

1
2

# use the /reranking endpoint
llama-server -m model.gguf --reranking

Constrain all outputs with a grammar

# custom grammar
llama-server -m model.gguf --grammar-file grammar.gbnf

# JSON
llama-server -m model.gguf --grammar-file grammars/json.gbnf

`llama-perplexity`

A tool for measuring the perplexity ¹ (and other quality metrics) of a model over a given text.

Measure the perplexity over a text file

llama-perplexity -m model.gguf -f file.txt

# [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
# Final estimate: PPL = 5.4007 +/- 0.67339

Measure KL divergence
1

# TODO

`llama-bench`

Benchmark the performance of the inference for various parameters.

Run default benchmark

llama-bench -m model.gguf

# Output:
# | model               |       size |     params | backend    | threads |          test |                  t/s |
# | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
#
# build: 3e0ba0e60 (4229)

`llama-run`

A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama ².

Run a model with a specific prompt (by default it's pulled from Ollama registry)
1

llama-run granite-code

`llama-simple`

A minimal example for implementing apps with `llama.cpp`. Useful for developers.

Basic text completion

1
2
3

llama-simple -m model.gguf

# Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of

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!
See good first issues for tasks suitable for first contributions
Read the CONTRIBUTING.md for more information
Make sure to read this: Inference at the edge
A bit of backstory for those who are interested: Changelog podcast

XCFramework

The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, and macOS. It can be used in Swift projects without the need to compile the library from source. For example:

// swift-tools-version: 5.10
// The swift-tools-version declares the minimum version of Swift required to build this package.

import PackageDescription

let package = Package(
    name: "MyLlamaPackage",
    targets: [
        .executableTarget(
            name: "MyLlamaPackage",
            dependencies: [
                "LlamaFramework"
            ]),
        .binaryTarget(
            name: "LlamaFramework",
            url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
            checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
        )
    ]
)

The above example is using an intermediate build b5046 of the library. This can be modified to use a different version by changing the URL and checksum.

Completions

Command-line completion is available for some environments.

Bash Completion

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$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
$ source ~/.llama-completion.bash

Optionally this can be added to your .bashrc or .bash_profile to load it automatically. For example:

`1`	`$ echo "source ~/.llama-completion.bash" >> ~/.bashrc`

Dependencies

yhirose/cpp-httplib - Single-header HTTP server, used by llama-server - MIT license
stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
minja - Minimal Jinja parser in C++, used by various tools/examples - MIT License
linenoise.cpp - C++ library that provides readline-like line editing capabilities, used by llama-run - BSD 2-Clause License
curl - Client-side URL transfer library, used by various tools/examples - CURL License
miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain

gpt4all

Thu, 14 Aug 2025 15:31:32 +0800

nomic-ai/gpt4all

GPT4All

Now with support for DeepSeek R1 Distillations

Website • Documentation • Discord • YouTube Tutorial

GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.

No API calls or GPUs required - you can just download the application and get started.

Read about what's new in our blog.

Subscribe to the newsletter

https://github.com/nomic-ai/gpt4all/assets/70534565/513a0f15-4964-4109-89e4-4f9a9011f311

GPT4All is made possible by our compute partner Paperspace.

Download Links

— Windows Installer —

— Windows ARM Installer —

— macOS Installer —

— Ubuntu Installer —

The Windows and Linux builds require Intel Core i3 2nd Gen / AMD Bulldozer, or better.

The Windows ARM build supports Qualcomm Snapdragon and Microsoft SQ1/SQ2 processors.

The Linux build is x86-64 only (no ARM).

The macOS build requires Monterey 12.6 or newer. Best results with Apple Silicon M-series processors.

See the full System Requirements for more details.

Flathub (community maintained)

Install GPT4All Python

gpt4all gives you access to LLMs with our Python client around llama.cpp implementations.

Nomic contributes to open source software like llama.cpp to make LLMs accessible and efficient for all.

`1`	`pip install gpt4all`

from gpt4all import GPT4All
model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf") # downloads / loads a 4.66GB LLM
with model.chat_session():
    print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024))

Integrations

:parrot::link: Langchain :card_file_box: Weaviate Vector Database - module docs :telescope: OpenLIT (OTel-native Monitoring) - Docs

Release History

July 2nd, 2024: V3.0.0 Release
- Fresh redesign of the chat application UI
- Improved user workflow for LocalDocs
- Expanded access to more model architectures
October 19th, 2023: GGUF Support Launches with Support for:
- Mistral 7b base model, an updated model gallery on our website, several new local code models including Rift Coder v1.5
- Nomic Vulkan support for Q4_0 and Q4_1 quantizations in GGUF.
- Offline build support for running old versions of the GPT4All Local LLM Chat Client.
September 18th, 2023: Nomic Vulkan launches supporting local LLM inference on NVIDIA and AMD GPUs.
July 2023: Stable support for LocalDocs, a feature that allows you to privately and locally chat with your data.
June 28th, 2023: Docker-based API server launches allowing inference of local LLMs from an OpenAI-compatible HTTP endpoint.

Contributing

GPT4All welcomes contributions, involvement, and discussion from the open source community! Please see CONTRIBUTING.md and follow the issues, bug reports, and PR markdown templates.

Check project discord, with project owners, or through existing issues/PRs to avoid duplicate work. Please make sure to tag all of the above with relevant project identifiers or your contribution could potentially get lost. Example tags: backend, bindings, python-bindings, documentation, etc.

Citation

If you utilize this repository, models or data in a downstream project, please consider citing it with:

@misc{gpt4all,
  author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},
  title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
}

GGUF on Producthunt daily

llama.cpp

llama.cpp

Recent API changes

Hot topics

Quick start

Description

Text-only

Multimodal

Supported backends

Obtaining and quantizing models

A CLI tool for accessing and experimenting with most of llama.cpp’s functionality.

A lightweight, OpenAI API compatible, HTTP server for serving LLMs.

A tool for measuring the perplexity 1 (and other quality metrics) of a model over a given text.

Benchmark the performance of the inference for various parameters.

A comprehensive example for running llama.cpp models. Useful for inferencing. Used with RamaLama 2.

A minimal example for implementing apps with llama.cpp. Useful for developers.

Contributing

Other documentation

Development documentation

Seminal papers and background on the models

XCFramework

Completions

Bash Completion

Dependencies

gpt4all

GPT4All

Download Links

Install GPT4All Python

Integrations

Release History

Contributing

Citation

A CLI tool for accessing and experimenting with most of `llama.cpp`’s functionality.

A tool for measuring the perplexity ¹ (and other quality metrics) of a model over a given text.

A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama ².

A minimal example for implementing apps with `llama.cpp`. Useful for developers.