GGUF is a quantized-model format designed for llama.cpp-style local inference. It replaced GGML as llama.cpp’s supported format and packages a model as one distributable file. section 6.4.2
GGUF’s practical advantages include:
- Embedding tokenizer information removes the need for an external JSON file. section 6.4.2
- Storing hyperparameters as key–value metadata makes it easier to add fields without breaking existing models. section 6.4.2
- Supporting local tools such as Ollama, LM Studio, and Jan enables offline inference on personal devices. section 12.7
Local deployment
Convert a custom model with llama.cpp’s convert_hf_to_gguf.py script, define its templates and parameters in an Ollama Modelfile, then serve it locally through Ollama. Jan instead requires GGUF models and reports hardware compatibility during selection. section 12.7


