@inproceedings{zhao-etal-2026-unleashing-low,
title = "Unleashing Low-Bit Inference on Ascend {NPU}s: A Comprehensive Evaluation of {H}i{F}loat Formats",
author = "Zhao, Pengxiang and
Zhen, Hui-Ling and
Li, Xing and
Bao, Han and
Lin, Weizhe and
Yang, Zhiyuan and
Wei, Yu Zi and
Wang, Xin and
Yuan, Mingxuan and
Yu, Xianzhi and
Dong, Zhenhua",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.57/",
pages = "844--864",
ISBN = "979-8-89176-394-4",
abstract = "As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family of formats tailored for Ascend NPUs. Through rigorous comparison across weight-activation and KV-cache tasks, we provide three key insights: (1) INT8 suits narrow-range data, while floating-point formats excel with high-variance data; (2) in 4-bit regimes, HiF4{'}s hierarchical scaling prevents the accuracy collapse seen in integer formats; and (3) HiFloat is fully compatible with state-of-the-art post-training quantization frameworks. Overall, HiFloat provides a solution for high-efficiency LLM inference on NPUs."
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%0 Conference Proceedings
%T Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
%A Zhao, Pengxiang
%A Zhen, Hui-Ling
%A Li, Xing
%A Bao, Han
%A Lin, Weizhe
%A Yang, Zhiyuan
%A Wei, Yu Zi
%A Wang, Xin
%A Yuan, Mingxuan
%A Yu, Xianzhi
%A Dong, Zhenhua
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zhao-etal-2026-unleashing-low
%X As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family of formats tailored for Ascend NPUs. Through rigorous comparison across weight-activation and KV-cache tasks, we provide three key insights: (1) INT8 suits narrow-range data, while floating-point formats excel with high-variance data; (2) in 4-bit regimes, HiF4’s hierarchical scaling prevents the accuracy collapse seen in integer formats; and (3) HiFloat is fully compatible with state-of-the-art post-training quantization frameworks. Overall, HiFloat provides a solution for high-efficiency LLM inference on NPUs.
%U https://aclanthology.org/2026.acl-industry.57/
%P 844-864
Markdown (Informal)
[Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats](https://aclanthology.org/2026.acl-industry.57/) (Zhao et al., ACL 2026)
ACL
- Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, and Zhenhua Dong. 2026. Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 844–864, San Diego, California, USA. Association for Computational Linguistics.