---
id: 20260607-T0-02
title: "图引导超低位量化降低大模型隐藏计算开销"
title_en: "Graph-guided ultra-low-bit quantization reduces LLM hidden costs"
url: https://ai.daily.yangsir.net/daily/20260607-T0-02
issue_date: 2026-06-07
publish_date: 2026-06-06T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2606.05429
---

# 图引导超低位量化降低大模型隐藏计算开销

arXiv 论文提出图引导的超低位量化方法，解决大模型训练后量化中的隐藏缩放开销问题。传统方法依赖刚性权重假设或位置启发式，引入额外计算负担。新方案通过图结构优化量化过程，在保持精度的同时显著降低 2-4 位量化时的隐藏成本。

## English Version

**Graph-guided ultra-low-bit quantization reduces LLM hidden costs**

arXiv paper proposes graph-guided ultra-low-bit quantization to solve hidden scaling overhead in LLM post-training quantization. Traditional methods introduce extra costs via rigid weight assumptions. The new approach optimizes quantization using graph structures, reducing hidden costs in 2-4 bit quantization while maintaining accuracy.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2606.05429)

**详情页**：https://ai.daily.yangsir.net/daily/20260607-T0-02

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