This research addresses key efficiency challenges in Mixture-of-Experts (MoE) language models by introducing innovative compression techniques that maintain performance while reducing resource requirements.

• Mixture Compressor: Novel approach that intelligently compresses experts based on their activation patterns
• Parameter Reduction: Achieves significant memory savings through expert compression and redundancy elimination
• Performance Preservation: Maintains model capabilities despite reduced parameters
• Practical Application: Enables more efficient deployment of large language models in resource-constrained environments

This engineering advancement makes cutting-edge MoE models more practical for real-world deployment by addressing their two biggest limitations: excessive memory consumption and redundant expert activations.

Mixture Compressor for Mixture-of-Experts LLMs Gains More