This research tackles a critical performance bottleneck in Mixture of Experts (MoE) architectures by intelligently managing token distribution across experts.

• Introduces Capacity-Aware Token Drop and Capacity-Aware Token Reroute techniques
• Reduces inference latency by preventing expert overloading
• Improves resource utilization by better balancing computation across experts
• Achieves up to 1.47x speedup without sacrificing model quality

For engineering teams deploying MoE-based LLMs, this approach offers practical solutions to address inference inefficiency problems that currently limit throughput in production systems.

Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts