# LLM Quantization Methods: A Comprehensive Comparative Analysis

> Source: <https://deepresearch.ninja/2026/07/LLM-Quantization-Methods-A-Comprehensive-Comparative-Analysis/>
> Published: 2026-07-03 00:00:00+00:00

Large language model quantization has evolved from a niche optimization technique into a critical deployment infrastructure. This report analyzes 15+ quantization methods across three dimensions: (1) algorithmic methodology, (2) performance characteristics, and (3) ecosystem maturity. The landscape divides into four primary paradigms: **CPU-optimized GGUF-based approaches** (llama.cpp k-quants, I-quants, curated by bartowski), **GPU-native weight-only kernels** (AWQ, GPTQ with Marlin/EXL2 backends), **NVIDIA’s floating-point hierarchy** (FP8, NVFP4 on Blackwell hardware, rooted in the APEX mixed-precision legacy), and **novel calibration-free methods** (HQQ, AutoRound).