LLaMA C++

llama.cpp is a high-performance, open-source C/C++ library for running large language models locally, with minimal dependencies and efficient inference on both CPU and GPU. It's the engine behind much of the local-AI ecosystem — tools like Ollama and LM Studio are built on top of it, even when users never interact with llama.cpp directly.

Common Use Cases

llama.cpp is widely used for:

• Local inference infrastructure: Powering other local-AI tools and applications under the hood.
• Resource-constrained deployment: Running capable models on modest hardware, including CPU-only setups.
• Custom integrations: Embedding LLM inference directly into other applications via its C/C++ core.
• Research & experimentation: A widely used reference implementation for efficient LLM inference.

Key Features & Capabilities

• Minimal dependencies: A lightweight, portable C/C++ codebase that runs across a wide range of hardware.
• Broad model support: Runs LLaMA, Mistral, Qwen, and many other open-weight model families.
• Quantization support: Reduces model size and memory requirements while preserving usable quality.
• CPU and GPU inference: Efficient execution without requiring high-end hardware.
• Foundation for higher-level tools: Powers the inference layer behind several popular local-AI applications.

Available Platforms

llama.cpp can be used through:

• Direct compilation: Build and run from source on macOS, Windows, and Linux.
• Command-line interface: Run models directly via its CLI tools.
• As a library: Embedded within other applications and tools.

Learn More

Official Resources

• llama.cpp on GitHub