---
id: 20260528-T0-09
title: "InfoQuant：重塑激活值分布，解决低比特大模型量化瓶颈"
title_en: "InfoQuant Reshapes Activations for Low-Bit LLM Quantization"
url: https://ai.daily.yangsir.net/daily/20260528-T0-09
issue_date: 2026-05-28
publish_date: 2026-05-27T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.26175
---

# InfoQuant：重塑激活值分布，解决低比特大模型量化瓶颈

低比特激活值量化一直是大模型高效部署的主要瓶颈。难点在于激活值包含异常值，且分布通常难以匹配低比特均匀量化。InfoQuant提出了一种新的方法来调整激活值分布，使其更适合低比特量化。该方法有效解决了异常值干扰问题，在不牺牲模型精度的前提下，大幅降低了大模型的内存占用和推理成本。开发者可借此在边缘设备上更流畅地运行大语言模型。

## English Version

**InfoQuant Reshapes Activations for Low-Bit LLM Quantization**

Low-bit activation quantization is a major bottleneck in efficient LLM deployment. InfoQuant reshapes activation distributions to fit low-bit uniform quantization better. It reduces memory footprint and inference costs without sacrificing accuracy, enabling smoother LLM deployment on edge devices.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.26175)

**详情页**：https://ai.daily.yangsir.net/daily/20260528-T0-09

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