gguf
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Best for Consumer hardware deploymentWorks with GitHubLow risk
⌘source
- author
@NousResearch- language
- Python
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Apr 17, 2026
✦overview.md
Key Features
- ·Universal hardware support (CPU, Apple Silicon, NVIDIA, AMD)
- ·Pure C/C++ inference without Python runtime
- ·Flexible 2-8 bit quantization with K-quants
- ·Ecosystem integration with LM Studio, Ollama, koboldcpp
- ·Importance matrix (imatrix) for better low-bit quality
Use Cases
- →Deploying models on consumer hardware like laptops and desktops
- →Running inference on Apple Silicon with Metal acceleration
- →CPU-based inference without GPU requirements
- →Using local AI tools like LM Studio, Ollama, or text-generation-webui
Best for
- ✓Consumer hardware deployment
- ✓Apple Silicon optimization
- ✓CPU-only inference environments
Not ideal for
- !Maximum accuracy scenarios requiring AWQ/GPTQ
- !Fast calibration-free quantization needs (use HQQ instead)
FAQs
name
gguf-quantization
description
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
license
MIT
version:1.0.0
author:Orchestra Research
dependencies:["llama-cpp-python>=0.2.0"]
metadata:{"hermes":{"tags":["GGUF","Quantization","llama.cpp","CPU Inference","Apple Silicon","Model Compression","Optimization"]}}
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
$install
1-click copynpx skills add NousResearch/hermes-agent --skill ggufSafety assessment
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