llama.cpp
FreeLLM inference in C/C++.
FreeFree tier
Inputs: textOutputs: text
About llama.cpp
llama.cpp is a high-performance, open-source library for large language model (LLM) inference written entirely in C/C++ with no external dependencies. It is designed for minimal setup and state-of-the-art performance across a wide range of hardware—from local machines to cloud instances. The project prioritizes Apple Silicon as a first-class citizen, supports multimodal models (e.g., vision-language), offers native WebGPU support for browser-based inference, and leverages the GGUF format for efficient model storage and quantization. It includes llama-cli for local inference and llama-server for an OpenAI-compatible API, along with Hugging Face integration for easy model downloads.
Key Features
Plain C/C++ implementation with zero external dependencies
Optimized for Apple Silicon as a first-class citizen
Multimodal support including vision-language models
WebGPU support enabling inference in the browser
GGUF model format for efficient quantization and storage
Hugging Face integration for direct model downloads and caching
llama-cli for local command-line inference
llama-server providing an OpenAI-compatible API
Support for multiple quantization methods (e.g., MXFP4, Q4_0, Q8_0)
Cross-platform: macOS, Linux, Windows, and Docker deployment
Pros & Cons
Pros
- Zero external dependencies simplifies building and deployment
- Exceptional performance on Apple Silicon hardware
- Active open-source community with frequent updates and fixes
- Supports a wide variety of model architectures and sizes
- Offers multiple quantization options to fit different hardware constraints
- Provides both CLI and server interfaces for flexible usage
- Multimodal and WebGPU capabilities enable advanced applications
Cons
- Requires compilation from source for best performance on some platforms
- Command-line interface may be less accessible to non-technical users
- Quantization trade-offs can affect model output quality
- Some advanced features (e.g., multimodal) require model-specific support
Best For
Running large language models locally on personal computers without cloud dependenciesDeploying LLMs on servers for production inference via OpenAI-compatible APIExperimenting with model quantization to reduce memory and compute requirementsBuilding multimodal applications that process text and images simultaneouslyEmbedding LLM inference into browser-based applications using WebGPUFine-tuning and testing custom models with efficient GGUF formatIntegrating with development tools like VS Code and Vim for code completion
FAQ
What is llama.cpp?
llama.cpp is an open-source library for LLM inference written in C/C++ with no dependencies, designed for high performance on local and cloud hardware.
How do I install llama.cpp?
You can install via brew, nix, winget, conda-forge, Docker, download pre-built binaries, or build from source by cloning the repository.
What models can I run with llama.cpp?
llama.cpp supports many model architectures in the GGUF format, including LLaMA, Mistral, Gemma, and multimodal models like LLaVA.
Does llama.cpp support GPU acceleration?
Yes, it supports acceleration on Apple Silicon (Metal), NVIDIA CUDA, AMD ROCm, and Vulkan, as well as WebGPU for browser inference.
What is the GGUF format?
GGUF is a binary format for storing quantized LLM models, designed for efficient loading and inference in llama.cpp.