ConDense-MoE introduces a novel approach to reduce memory requirements in large language models while preserving performance, addressing a critical barrier to practical LLM deployment.

• Achieves better efficiency-performance trade-offs than simply pruning MoE layers
• Condenses multiple experts into fewer, more capable experts rather than just removing them
• Maintains model performance with significantly reduced memory footprint
• Enables practical deployment of powerful models in memory-constrained environments

This engineering breakthrough makes advanced LLMs more accessible for real-world applications by reducing hardware requirements without sacrificing capabilities.

Original Paper: Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning