What Is GGUF?
GGUF is a binary model format used in the GGML/llama.cpp ecosystem. It can store model tensors and metadata in a single file and supports quantized tensor types.
What GGUF is
GGUF is a binary file format used by GGML-based software. In the local-LLM world it is especially associated with llama.cpp.
The llama.cpp documentation states that models used by the project are stored in GGUF and provides conversion scripts for supported models in other formats. Hugging Face likewise describes GGUF as a single-file format for storing models for GGML inference.
What is inside a GGUF file?
At a high level, a GGUF file contains metadata plus tensor information/data. The llama.cpp GGUF header documentation describes a structured binary file with a version, key-value metadata, tensor descriptors and the tensor data itself.
That is why a GGUF filename can communicate more than “this is a model.” It may also identify a quantization variant, while the file itself carries metadata needed by compatible tooling.
A Llama-family model, Gemma-family model or another supported architecture may be distributed as GGUF. The file format and the neural-network architecture are different concepts.
How GGUF relates to quantization
GGUF supports multiple tensor data types, including quantized representations. A model can therefore be converted into GGUF and then distributed in several quantization variants.
This explains downloads such as ...Q4_K_M.gguf: GGUF tells you the container/model format; Q4_K_M describes the quantization variant used for the weights.
Hugging Face's Transformers documentation also supports loading GGUF files and notes that GGUF can contain quantized data types that reduce memory use.
Does every AI runtime use GGUF?
No. GGUF is important in the llama.cpp ecosystem, but local AI is broader than one format. Other inference stacks can use other model representations.
Choose a model format based on the runtime you plan to use. If your workflow is built around llama.cpp or software that uses its ecosystem, GGUF is central. If you are using another inference engine, verify that engine's supported formats instead of converting files unnecessarily.