EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models

• URL: http://arxiv.org/abs/2508.01625v1
• Date: Sun, 03 Aug 2025 07:30:42 GMT
• Title: EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
• Authors: Yuanteng Chen, Yuantian Shao, Peisong Wang, Jian Cheng,
• Abstract summary: Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs.<n>It is hindered by two critical challenges: (1) substantial memory consumption to load all experts; and (2) low activated parameters cannot be equivalently translated into inference acceleration effects.<n>We propose an Expert-Selection Aware for MoE-LLMs, which deeply aligns with the characteristics of MoE from the perspectives of quantization and pruning.
• Score: 18.870990552728948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs. However, it is hindered by two critical challenges: (1) substantial GPU memory consumption to load all experts; (2) low activated parameters cannot be equivalently translated into inference acceleration effects. In this work, we propose EAC-MoE, an Expert-Selection Aware Compressor for MoE-LLMs, which deeply aligns with the characteristics of MoE from the perspectives of quantization and pruning, and introduces two modules to address these two challenges respectively: (1) The expert selection bias caused by low-bit quantization is a major factor contributing to the performance degradation in MoE-LLMs. Based on this, we propose Quantization with Expert-Selection Calibration (QESC), which mitigates the expert selection bias by calibrating the routers within the MoE; (2) There are always certain experts that are not crucial for the corresponding tasks, yet causing inference latency. Therefore, we propose Pruning based on Expert-Selection Frequency (PESF), which significantly improves inference speed by pruning less frequently used experts for current task. Extensive experiments demonstrate that our approach significantly reduces memory usage and improves inference speed with minimal performance degradation.

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