GGUF is a single-file format for storing AI models so they can be loaded quickly and run by llama.cpp and other GGML-based tools.
In plain words
Models are usually trained in frameworks such as PyTorch, then converted for running elsewhere. GGUF is the format used by the GGML library and llama.cpp. One binary file holds the model's weights plus metadata, such as its settings, in a key-value structure, so it loads fast and new information can be added without breaking older files. Models can also be quantized into GGUF, meaning weights are stored at lower precision to save memory.
A quick example
Imagine you want to run an open model on your laptop. You download a GGUF version of it from Hugging Face, choosing a smaller quantized file that fits your memory, and load it with llama.cpp or an app built on it. The one file is all you need.
Where you'll meet it
- Running LLMs locally with llama.cpp and similar tools.
- Sharing quantized open-weight models on Hugging Face.
Related
Sources
- DocsGGUF, GGML (docs on GitHub) · read 29 Sept 2026
- DocsGGUF, Hugging Face Hub documentation · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.