Gguf Quantization

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

This is your go-to for converting models to GGUF and running them with llama.cpp, especially if you're deploying on Apple Silicon, consumer hardware, or need CPU inference. The skill covers the full workflow: converting HuggingFace models, quantizing with K-quant methods (Q4_K_M is the recommended default), and using importance matrices for better quality at lower bits. It includes both CLI and Python examples with llama-cpp-python, plus server mode for OpenAI-compatible APIs. The real value is in the practical workflows and the quantization comparison table showing actual size/quality tradeoffs. If you need maximum speed on NVIDIA in production, you'd want TensorRT-LLM instead, but for flexible deployment across hardware without GPU requirements, this covers everything.

skills.sh installs: 1.1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill gguf-quantization --agent claude-code

Installs into .claude/skills of the current project.

Files

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.