Microscaling Floating-Point (MXFP) has emerged as a promising low-precision format for large language models (LLMs). Despite various post-training quantization (PTQ) algorithms being proposed, they mostly focus on integer quantization, while their applicability and behavior under MXFP formats remain largely unexplored. To address this gap, this work conducts a systematic investigation of PTQ under MXFP formats, encompassing over 7 PTQ algorithms, 15 evaluation benchmarks, and 3 LLM families. The key findings include: 1) MXFP8 consistently achieves near-lossless performance, while MXFP4 introduces substantial accuracy degradation and remains challenging; 2) PTQ effectiveness under MXFP depends strongly on format compatibility, with some algorithmic paradigms being consistently more effective than others; 3) PTQ performance exhibits highly consistent trends across model families and modalities, in particular, quantization sensitivity is dominated by the language model rather than the vision encoder in multimodal LLMs; 4) The scaling factor of quantization is a critical error source in MXFP4, and a simple pre-scale optimization strategy can significantly mitigate its impact. Together, these results provide practical guidance on adapting existing PTQ methods to MXFP quantization.

翻译：微缩浮点（MXFP）已成为大语言模型（LLMs）中一种有前景的低精度格式。尽管已提出多种训练后量化（PTQ）算法，但它们主要集中于整数量化，而在MXFP格式下的适用性和行为仍很大程度上未被探索。为填补这一空白，本研究对MXFP格式下的PTQ进行了系统性调查，涵盖超过7种PTQ算法、15个评估基准和3个LLM系列。主要发现包括：1）MXFP8始终能实现近乎无损的性能，而MXFP4则带来显著的精度损失且仍具挑战性；2）MXFP下的PTQ效果高度依赖于格式兼容性，某些算法范式始终比其他方法更有效；3）PTQ性能在不同模型系列和模态间表现出高度一致的趋势，特别是，在多模态LLMs中，量化敏感性主要由语言模型而非视觉编码器主导；4）量化缩放因子是MXFP4中的关键误差源，而一种简单的预缩放优化策略能显著减轻其影响。这些结果共同为将现有PTQ方法适配至MXFP量化提供了实用指导。