GS-MoE framework offers training-free compression for MoE models

Researchers have developed GS-MoE, a novel training-free framework designed to compress Mixture-of-Experts (MoE) models. This method tackles the memory bottleneck caused by storing numerous expert parameters by employing groupwise subspace sharing and pruning techniques. GS-MoE captures shared structures across experts while allowing for expert-specific projections and reducing intermediate widths within each expert. Experiments show that GS-MoE significantly outperforms existing methods like D$^2$-MoE, particularly at high compression rates, retaining a higher percentage of reasoning performance and achieving lower perplexity on models like DeepSeek-MoE-16B-Base. AI

IMPACT This research offers a method to reduce the memory footprint of large MoE models, potentially making them more accessible and efficient for deployment.

RANK_REASON The cluster contains a research paper detailing a new method for compressing AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• DeepSeek-MoE-16B-Base
• GS-MoE
• Hugging Face
• Mixture-of-Experts

• paper
• infra

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