DSMoE is a novel approach that partitions pre-trained neural network layers into computational blocks with dynamic routing, reducing computation without sacrificing model capabilities.

• Enables computation-efficient LLMs without losing model knowledge
• Implements adaptive expert routing via sigmoid activation
• Achieves performance comparable to dense models with reduced computational costs
• Offers practical solution to LLM scaling challenges

This engineering breakthrough addresses a critical challenge in AI deployment: maintaining model quality while reducing resource requirements, making advanced LLMs more accessible and commercially viable.

DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs