The easiest way to run an open model on your own machine: one command downloads and starts it, plus a local REST API and libraries for Python and JavaScript.
All repositories
Open models & inferenceggml-org
llama.cpp
A C/C++ engine that runs language models on ordinary hardware: laptops, phones, GPU-less servers. Much of local AI, Ollama included, is built on it.
Who it is for
For people who need lightweight, fast local inference without heavy dependencies.
How to start
- Install:
curl -LsSf https://llama.app/install.sh | sh. - Run a model from Hugging Face:
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF. - Start an OpenAI-compatible API:
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF.
Steps are taken from the README. Check the current version in the repository before running them.
Stars over the last 30 days
+3,622Sep 6 — Oct 6
Author's description
LLM inference in C/C++
More in «Open models & inference»
The core library for working with models: one codebase for text, vision, audio and multimodal tasks, for both inference and training. Most new open models ship through it.
A self-hosted ChatGPT-style interface that connects to Ollama and any OpenAI-compatible API. Installs with a single Docker command and runs on your own server.
Repository of the large open DeepSeek-V3 mixture-of-experts model: description, benchmark results and run instructions. It showed an open model can stand next to closed ones.
The foundation under most modern neural networks: GPU-accelerated tensors and automatic differentiation, wrapped in ordinary Python. Transformers, Llama and nearly every open model are written on it.
An engine for fast model serving on GPUs: frugal with memory and handles many requests at once. The default pick when a model has to run as a service.