llama.cpp

C/C++ inference engine for running LLMs on ordinary hardware

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What is llama.cpp?

llama.cpp is an open-source inference engine written in C and C++ that runs quantised language models on CPUs, GPUs and Apple Silicon. It introduced the GGUF model format, which packs weights and metadata into a single file, and it runs with no Python and very few dependencies. Most local chat applications, including several desktop tools, are built on it or its libraries. The differentiator is reachability: it runs useful models on hardware that no GPU-only stack would accept, including laptops, small boards and phones.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

GGUF model format with many quantisation levels
CPU, CUDA, Metal, Vulkan and ROCm backends in one codebase
Command-line chat, completion and server binaries
OpenAI-compatible local HTTP server mode for app integration
Grammar-constrained output and JSON schema support
Multimodal support for several vision-language models

Pros & cons

Strengths

Runs on modest hardware where GPU stacks cannot
Single-file models and almost no runtime dependencies
Very active development and a large community

Watch out for

Build steps and flags are unfriendly to non-developers
Throughput trails dedicated GPU servers on large models
Quantisation trades some answer quality for memory

Best for & use cases

local inference, edge deployment, cpu-only serving, offline embeddings

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FAQ

What is GGUF?

It is the model file format llama.cpp introduced. A single file holds the quantised weights and the metadata needed to run them, which makes local models easy to download and share.

How much memory do I need?

Quantised 7B models run in roughly 4-8GB, and larger models scale from there. CPU inference works for small models, but a GPU or Apple Silicon chip makes a large difference to speed.

Is it meant for production servers?

It can serve requests through its OpenAI-compatible server, and it is common on edge devices. For high-throughput serving on GPUs, dedicated engines usually deliver more requests per second.