---
id: 20260618-T0-11
title: "MODE：MoE多模态模型量化新方案"
title_en: "MODE Quantizes MoE Multimodal LLMs"
url: https://ai.daily.yangsir.net/daily/20260618-T0-11
issue_date: 2026-06-18
publish_date: 2026-06-17T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2606.17118
---

# MODE：MoE多模态模型量化新方案

arXiv论文提出MODE方法，解决MoE多模态模型内存占用问题。通过专家级混合精度量化，模型内存需求降低60% while保持95%性能。适用于GPT-4V、Claude 3等大型模型，已在开源库实现。

## English Version

**MODE Quantizes MoE Multimodal LLMs**

arXiv paper introduces MODE, reducing MoE multimodal LLM memory usage by 60% while maintaining 95% performance through expert-level mixed-precision quantization. Applicable to GPT-4V and Claude 3, it's implemented in open-source libraries.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2606.17118)

**详情页**：https://ai.daily.yangsir.net/daily/20260618-T0-11

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