---
id: 20260305-T0-16
title: "MoE模型需路由器校准以高效压缩"
title_en: "MoE Models Need Router Calibration for Efficient Compression"
url: https://ai.daily.yangsir.net/daily/20260305-T0-16
issue_date: 2026-03-05
publish_date: 2026-03-04T05:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2603.02217
---

# MoE模型需路由器校准以高效压缩

研究指出，混合专家模型（MoE）虽能高效扩展，但存在部署时内存瓶颈。团队提出无需重训练的三类压缩范式：专家剪枝、专家编辑和专家共享。实验证明，路由器校准可将MoE模型推理速度提升40%，同时保持92%性能。该方法适用于大规模AI模型部署，开发者可据此优化资源利用率。

## English Version

**MoE Models Need Router Calibration for Efficient Compression**

Research shows MoE models face memory bottlenecks during deployment despite efficient scaling. The team introduces three compression paradigms—expert pruning, editing, and sharing—without retraining. Router calibration improves MoE inference speed by 40% while maintaining 92% performance, optimizing resource utilization for large-scale AI model deployment.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2603.02217)

**详情页**：https://ai.daily.yangsir.net/daily/20260305-T0-16

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