  Large Language Models (LLMs) have distinguished themselves with outstanding
performance in complex language modeling tasks, yet they come with significant
computational and storage challenges. This paper explores the potential of
quantization to mitigate these challenges. We systematically study the combined
application of two well-known post-training techniques, SmoothQuant and GPTQ,
and provide a comprehensive analysis of their interactions and implications for
advancing LLM quantization. We enhance the versatility of both techniques by
enabling quantization to microscaling (MX) formats, expanding their
applicability beyond their initial fixed-point format targets. We show that by
applying GPTQ and SmoothQuant, and employing MX formats for quantizing models,
we can achieve a significant reduction in the size of OPT models by up to 4x
and LLaMA models by up to 3x with a negligible perplexity increase of 1-3%.
