ggml-org/llama.cpp
Llama.cpp
Llama.cpp is an inference engine designed for the local execution of text-based and multimodal language models on consumer hardware. It provides a core environment for running models that process both text and image inputs, utilizing hardware-accelerated backends to optimize performance across diverse CPU and GPU architectures.
The project distinguishes itself by offering a lightweight HTTP server that adheres to standard API specifications, enabling chat completion, embeddings, and reranking services. It includes a suite of tools for model quantization and conversion, which reduces memory usage and improves performance, alongside a command-line interface for managing chat templates and inference parameters.
The ecosystem further supports structured data generation through grammar-based output constraints and provides diagnostic utilities for visualizing computational graphs. Comprehensive documentation is available, including a reference matrix that details the compatibility of computational operations across supported hardware backends.
Features
- Hardware Abstraction Layers - Support for multiple hardware-accelerated backends to optimize model inference across diverse CPU and GPU architectures.
- Text-Only Inference Engines - A high-performance inference engine designed for running text-based language models locally on consumer hardware.
- Multimodal Inference Engines - An inference engine capable of local execution for vision-language models that process both text and image inputs.
- Inference API Servers - A lightweight HTTP server providing endpoints for chat completion, embeddings, and reranking that adheres to standard API specifications.
- Model Quantization Tools - Tools for converting and quantizing models into compressed formats to reduce memory usage and improve inference performance.
- Command Line Inference Interfaces - A command-line interface for executing models, managing chat templates, and configuring inference parameters in interactive or batch modes.