GGUF - Quantization Format for llama.cpp

The GGUF (GPT-Generated Unified Format) is the standard file format for llama.cpp, enabling efficient inference on CPUs, Apple Silicon, and GPUs with flexible quantization options.

When to use GGUF

Use GGUF when:

• Deploying on consumer hardware (laptops, desktops)
• Running on Apple Silicon (M1/M2/M3) with Metal acceleration
• Need CPU inference without GPU requirements
• Want flexible quantization (Q2_K to Q8_0)
• Using local AI tools (LM Studio, Ollama, text-generation-webui)

Key advantages:

• Universal hardware: CPU, Apple Silicon, NVIDIA, AMD support
• No Python runtime: Pure C/C++ inference
• Flexible quantization: 2-8 bit with various methods (K-quants)
• Ecosystem support: LM Studio, Ollama, koboldcpp, and more
• imatrix: Importance matrix for better low-bit quality

Use alternatives instead:

• AWQ/GPTQ: Maximum accuracy with calibration on NVIDIA GPUs
• HQQ: Fast calibration-free quantization for HuggingFace
• bitsandbytes: Simple integration with transformers library
• TensorRT-LLM: Production NVIDIA deployment with maximum speed

Quick start

Installation

# Clone llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp

# Build (CPU)
make

# Build with CUDA (NVIDIA)
make GGML_CUDA=1

# Build with Metal (Apple Silicon)
make GGML_METAL=1

# Install Python bindings (optional)
pip install llama-cpp-python

Convert model to GGUF

# Install requirements
pip install -r requirements.txt

# Convert HuggingFace model to GGUF (FP16)
python convert_hf_to_gguf.py ./path/to/model --outfile model-f16.gguf

# Or specify output type
python convert_hf_to_gguf.py ./path/to/model \
    --outfile model-f16.gguf \
    --outtype f16

Quantize model

# Basic quantization to Q4_K_M
./llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M