@inproceedings{ding-etal-2026-hyquant,
title = "{H}y{Q}uant: Hybrid-Precision Quantization for {LLM} Attention",
author = "Ding, Jiatong and
Xing, Bingxin and
Zhang, Yu and
Ding, Dian and
Yi, Xiaodong and
Ouyang, Xianbin and
Zhou, Fei Hu and
Zhang, Kun and
Guo, Zhenyu and
Pan, Hao and
Xue, Guangtao and
Zhang, Yiming",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1705/",
pages = "38834--38848",
ISBN = "979-8-89176-459-0",
abstract = "Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, low-bit quantization of the \textit{attention} module often introduces large errors at very low bit-widths, causing performance degradation. Existing methods mainly rely on smoothing techniques to handle outliers, while we propose a hybrid quantization design to better balance accuracy and efficiency. Specifically, we propose **HyQuant**, an efficient hybrid quantization framework for LLM attention. HyQuant quantizes most attention states into low-bit formats while retaining a small set of vertical-line tokens and local-window states in high precision. These accuracy-critical regions are selected using lightweight vertical-line-aware attention-pattern signals, reducing quantization error with limited overhead. In the Prefill stage, HyQuant uses a hybrid-precision quantized attention operator that preserves vertical-line tokens and a local sliding window in full precision while quantizing the remaining context. In the Decode stage, HyQuant applies the same principle to KV-cache compression and fuses KV dequantization with attention computation to improve memory and hardware efficiency. Across diverse tasks, models, and datasets, HyQuant maintains nearly lossless accuracy with a simple design, demonstrating the efficiency and practical feasibility of hybrid quantization for LLM attention."
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<abstract>Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, low-bit quantization of the attention module often introduces large errors at very low bit-widths, causing performance degradation. Existing methods mainly rely on smoothing techniques to handle outliers, while we propose a hybrid quantization design to better balance accuracy and efficiency. Specifically, we propose **HyQuant**, an efficient hybrid quantization framework for LLM attention. HyQuant quantizes most attention states into low-bit formats while retaining a small set of vertical-line tokens and local-window states in high precision. These accuracy-critical regions are selected using lightweight vertical-line-aware attention-pattern signals, reducing quantization error with limited overhead. In the Prefill stage, HyQuant uses a hybrid-precision quantized attention operator that preserves vertical-line tokens and a local sliding window in full precision while quantizing the remaining context. In the Decode stage, HyQuant applies the same principle to KV-cache compression and fuses KV dequantization with attention computation to improve memory and hardware efficiency. Across diverse tasks, models, and datasets, HyQuant maintains nearly lossless accuracy with a simple design, demonstrating the efficiency and practical feasibility of hybrid quantization for LLM attention.</abstract>
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%0 Conference Proceedings
%T HyQuant: Hybrid-Precision Quantization for LLM Attention
%A Ding, Jiatong
%A Xing, Bingxin
%A Zhang, Yu
%A Ding, Dian
%A Yi, Xiaodong
%A Ouyang, Xianbin
%A Zhou, Fei Hu
%A Zhang, Kun
%A Guo, Zhenyu
%A Pan, Hao
%A Xue, Guangtao
%A Zhang, Yiming
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ding-etal-2026-hyquant
%X Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, low-bit quantization of the attention module often introduces large errors at very low bit-widths, causing performance degradation. Existing methods mainly rely on smoothing techniques to handle outliers, while we propose a hybrid quantization design to better balance accuracy and efficiency. Specifically, we propose **HyQuant**, an efficient hybrid quantization framework for LLM attention. HyQuant quantizes most attention states into low-bit formats while retaining a small set of vertical-line tokens and local-window states in high precision. These accuracy-critical regions are selected using lightweight vertical-line-aware attention-pattern signals, reducing quantization error with limited overhead. In the Prefill stage, HyQuant uses a hybrid-precision quantized attention operator that preserves vertical-line tokens and a local sliding window in full precision while quantizing the remaining context. In the Decode stage, HyQuant applies the same principle to KV-cache compression and fuses KV dequantization with attention computation to improve memory and hardware efficiency. Across diverse tasks, models, and datasets, HyQuant maintains nearly lossless accuracy with a simple design, demonstrating the efficiency and practical feasibility of hybrid quantization for LLM attention.
%U https://aclanthology.org/2026.emnlp-main.1705/
%P 38834-38848
Markdown (Informal)
[HyQuant: Hybrid-Precision Quantization for LLM Attention](https://aclanthology.org/2026.emnlp-main.1705/) (Ding et al., EMNLP 2026)
ACL
- Jiatong Ding, Bingxin Xing, Yu Zhang, Dian Ding, Xiaodong Yi, Xianbin Ouyang, Fei Hu Zhou, Kun Zhang, Zhenyu Guo, Hao Pan, Guangtao Xue, and Yiming Zhang. 2026. HyQuant: Hybrid-Precision Quantization for LLM Attention. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38834–38848, Budapest, Hungary. Association for Computational Linguistics.