---
id: 20260807-T0-10
title: "循环残差量化：支持LLM多精度灵活部署的新方案"
title_en: "Recurrent Residual Quantization: Flexible Multi-Precision LLM Deployment"
url: https://ai.daily.yangsir.net/daily/20260807-T0-10
issue_date: 2026-08-07
publish_date: 2026-08-06T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2608.04048
---

# 循环残差量化：支持LLM多精度灵活部署的新方案

研究团队提出循环残差量化（Recurrent Residual Quantization），一种支持LLM多精度灵活部署的表示方法。传统量化方案需要为每种目标精度单独准备一个检查点，内存和存储开销大。新方法通过渐进的残差量化方案，让同一个模型可以动态适配不同精度需求，在精度、内存占用和吞吐量之间灵活权衡。这对于在多种硬件环境下部署大语言模型的工程团队有实际价值。

## English Version

**Recurrent Residual Quantization: Flexible Multi-Precision LLM Deployment**

Researchers proposed Recurrent Residual Quantization, a progressive multi-precision representation method for LLM deployment. Traditional quantization requires separate checkpoints for each target precision, consuming significant memory and storage. The new method uses progressive residual quantization to let a single model dynamically adapt to different precision requirements, flexibly trading off accuracy, memory, and throughput. This offers practical value for engineering teams deploying LLMs across diverse hardware environments.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2608.04048)

**详情页**：https://ai.daily.yangsir.net/daily/20260807-T0-10

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