Large machine learning models like LLMs rely on quantization techniques such as GPTQ, AWQ, and HQQ to reduce resource usage. By storing multiple elements in a single 32-bit element through bitpacking, models can effectively operate at low bits, even 2-bit. Gemlite offers user-friendly CUDA kernels for customized quantization methods, enabling significant speed improvements without compromising accuracy. Combining sparsity with quantization, as demonstrated in the 1:2 sparsity case study, can boost performance by up to 3.5x compared to Pytorch’s fp16 matmul. The innovative approach of compressing weights through sparsity and custom bitpacking showcases Gemlite’s adaptability and optimization potential.
https://mobiusml.github.io/gemlite_blogpost/