llama.cpp

llama.cpp is a high-performance C/C++ library for running large language model (LLM) inference locally on a wide variety of hardware. Originally developed to enable running Meta's LLaMA models on consumer hardware, it has evolved into a comprehensive framework supporting numerous model architectures and quantization formats. The project prioritizes efficiency, portability, and minimal dependencies, making it ideal for developers who want to deploy LLMs without relying on cloud services.

• Pure C/C++ Implementation provides a lightweight, dependency-free codebase that compiles easily across platforms without requiring heavy frameworks like PyTorch or TensorFlow.

• Extensive Quantization Support enables running large models on limited hardware through various quantization methods (4-bit, 5-bit, 8-bit), dramatically reducing memory requirements while maintaining reasonable quality.

• Multi-Platform Hardware Acceleration supports CUDA, Metal, OpenCL, Vulkan, and CPU-optimized SIMD instructions, allowing optimal performance on NVIDIA GPUs, Apple Silicon, AMD GPUs, and modern CPUs.

• Model Format Compatibility works with GGUF format and supports conversion from various model formats, enabling use of models from Hugging Face and other sources.

• Server Mode includes a built-in HTTP server with OpenAI-compatible API endpoints, making it easy to integrate into existing applications and workflows.

• Active Community Development benefits from rapid iteration and contributions from a large open-source community, with frequent updates adding support for new models and optimizations.

To get started, clone the repository, build using CMake with your preferred backend (CPU, CUDA, Metal, etc.), download a GGUF-format model, and run inference using the provided command-line tools or server. The project includes comprehensive documentation covering build options, model conversion, and API usage.