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cs.LG2026
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
Yifu Ding, Jiacheng Wang, Ge Yang +4
Mixture-of-Experts (MoE) models scale compute efficiently, yet remain expensive to deploy due to their substantial memory footprint and inference overhead. Prior compression method…
cs.LG2025
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
Lujun Li, Zhu Qiyuan, Jiacheng Wang +4
Mixture of Experts (MoE) LLMs face significant obstacles due to their massive parameter scale, which imposes memory, storage, and deployment challenges. Although recent expert merg…