llama.cpp is a high-performance inference library for Large Language Models (LLMs) implemented in C/C++. Designed to enable efficient and scalable LLM deployment across various hardware architectures, it supports minimal setup while maintaining state-of-the-art performance.
Key Features:
Multi-bit quantization support (1.5-bit, 2-bit, up to 8-bit) for optimized memory usage and faster inference.
GPU acceleration via CUDA, HIP (for AMD GPUs), and MUSA (Moore Threads GPUs).
CPU optimizations leveraging ARM NEON, Apple's Accelerate framework, and x86 instruction sets (AVX, AVX2, AVX512, AMX).
Support for a wide range of models, including LLaMA, Mistral, Falcon, Alpaca, and others.
Minimal runtime dependencies, ensuring ease of deployment.
Hybrid CPU-GPU inference to handle models larger than available GPU memory.
Audience & Benefit:
Ideal for developers and researchers seeking a lightweight yet powerful solution for integrating LLM capabilities into applications. llama.cpp enables seamless deployment across diverse hardware, from mobile devices to data centers, with minimal resource overhead. Its extensive model support and optimization features make it a versatile tool for advancing AI applications efficiently.
# 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.
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
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 /[:quant]. For example:
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
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
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 β 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
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.
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
# up to 4 concurrent requests, each with 4096 max context
llama-server -m model.gguf -c 16384 -np 4
Enable speculative decoding
# 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
# use the /embedding endpoint
llama-server -m model.gguf --embedding --pooling cls -ub 8192
Serve a reranking model
# use the /reranking endpoint
llama-server -m model.gguf --reranking
A minimal example for implementing apps with llama.cpp. Useful for developers.
Basic text completion
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 can push to branches in the llama.cpp repo and merge PRs into the master branch
Collaborators will be invited based on contributions
Any help with managing issues, PRs and projects is very appreciated!
If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
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.
