INT vs. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

Abstract

Modern AI hardware, such as Nvidia's Blackwell architecture, is increasingly embracing low-precision floating-point (FP) formats to handle the pervasive activation outliers in Large Language Models (LLMs). Despite this industry trend, a unified comparison of FP and integer (INT) quantization across varying granularities has been missing, leaving algorithm and hardware co-design without clear guidance. This paper fills that gap by systematically investigating the trade-offs between FP and INT formats. We reveal a critical performance crossover: while FP excels in coarse-grained quantization, INT consistently surpasses it as the quantization block size shrinks. Our comprehensive comparison demonstrates that for popular fine-grained formats like MX (block size 32), MXINT8 and MXINT4 are superior to their FP counterparts in both algorithmic accuracy and hardware efficiency. We also introduce a symmetric clipping method that resolves gradient bias in fine-grained low-bit INT training, enabling nearly lossless performance for MXINT8 training. These findings challenge the current hardware trajectory and advocate for prioritizing fine-grained INT formats in future AI accelerators to achieve a better balance of accuracy, power, and efficiency.