LLM Quantization Methods: A Comprehensive Comparative Analysis

A comprehensive analysis of 15+ large language model quantization methods categorizes them into four paradigms: CPU-optimized GGUF-based approaches, GPU-native weight-only kernels, NVIDIA's floating-point hierarchy, and novel calibration-free methods. The report evaluates each method across algorithmic methodology, performance characteristics, and ecosystem maturity, highlighting the evolution of quantization from a niche optimization technique to critical deployment infrastructure.

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 .