INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908619155439616 |
|---|---|
| author | Chen, Mengzhao Wu, Meng Jin, Hui Yuan, Zhihang Liu, Jing Zhang, Chaoyi Li, Yunshui Huang, Jie Ma, Jin Xue, Zeyue Liu, Zhiheng Bin, Xingyan Luo, Ping |
| author_facet | Chen, Mengzhao Wu, Meng Jin, Hui Yuan, Zhihang Liu, Jing Zhang, Chaoyi Li, Yunshui Huang, Jie Ma, Jin Xue, Zeyue Liu, Zhiheng Bin, Xingyan Luo, Ping |
| contents | 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, the comparison at fine-grained (block-wise) levels is more nuanced. Our comprehensive comparison demonstrates that for popular 8-bit fine-grained formats (e.g., MX with block size 32), MXINT8 is superior to its FP counterpart in both algorithmic accuracy and hardware efficiency. However, for 4-bit formats, FP (e.g., MXFP4, NVFP4) often holds an accuracy advantage , though we show that NVINT4 can surpass NVFP4 when outlier-mitigation techniques like Hadamard rotation are applied. 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, demonstrating that a one-size-fits-all FP approach is suboptimal and advocating that fine-grained INT formats, particularly MXINT8, offer a better balance of accuracy, power, and efficiency for future AI accelerators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats Chen, Mengzhao Wu, Meng Jin, Hui Yuan, Zhihang Liu, Jing Zhang, Chaoyi Li, Yunshui Huang, Jie Ma, Jin Xue, Zeyue Liu, Zhiheng Bin, Xingyan Luo, Ping Machine Learning Artificial Intelligence 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, the comparison at fine-grained (block-wise) levels is more nuanced. Our comprehensive comparison demonstrates that for popular 8-bit fine-grained formats (e.g., MX with block size 32), MXINT8 is superior to its FP counterpart in both algorithmic accuracy and hardware efficiency. However, for 4-bit formats, FP (e.g., MXFP4, NVFP4) often holds an accuracy advantage , though we show that NVINT4 can surpass NVFP4 when outlier-mitigation techniques like Hadamard rotation are applied. 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, demonstrating that a one-size-fits-all FP approach is suboptimal and advocating that fine-grained INT formats, particularly MXINT8, offer a better balance of accuracy, power, and efficiency for future AI accelerators. |
| title | INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.25602 |