MoDES: Enhancing Multimodal LLMs with Dynamic Expert Skipping for Speed
Published:Nov 19, 2025 18:48
•1 min read
•ArXiv
Analysis
This research focuses on optimizing the performance of Mixture-of-Experts (MoE) multimodal large language models, specifically by introducing dynamic expert skipping. The use of dynamic skipping likely reduces computational costs and inference time, which are key bottlenecks in large language model applications.
Key Takeaways
- •Focuses on improving the efficiency of MoE multimodal LLMs.
- •Employs dynamic expert skipping as a method for acceleration.
- •Addresses performance bottlenecks related to computational cost and inference time.
Reference
“The research aims to accelerate Mixture-of-Experts multimodal large language models.”