Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts
Shwai He, Weilin Cai, Jiayi Huang, Ang Li
2025-03-12
Summary
This paper talks about a smarter way to manage AI models that use teams of specialized mini-models (experts), fixing slowdowns caused when one expert gets overloaded while others sit idle.
What's the problem?
In AI systems that split work among expert sub-models, some experts get swamped with tasks, causing delays as the whole system waits for the busiest expert to finish.
What's the solution?
The solution skips or redirects extra tasks from overloaded experts to idle ones, like rerouting cars from a traffic jam to empty roads, speeding up the whole system without losing quality.
Why it matters?
This makes AI faster and cheaper to run, helping services like chatbots or translators respond quicker while using less energy and computer power.
Abstract
The Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation, optimizing the trade-off between performance and efficiency. However, under expert parallelism, MoE suffers from inference inefficiencies due to imbalanced token-to-expert assignment, where some experts are overloaded while others remain underutilized. This imbalance leads to poor resource utilization and increased latency, as the most burdened expert dictates the overall delay, a phenomenon we define as the \textit{Straggler Effect}. To mitigate this, we propose Capacity-Aware Inference, including two key techniques: (1) \textit{Capacity-Aware Token Drop}, which discards overloaded tokens to regulate the maximum latency of MoE, and (2) \textit{Capacity-Aware Token Reroute}, which reallocates overflowed tokens to underutilized experts, balancing the token distribution. These techniques collectively optimize both high-load and low-load expert utilization, leading to a more efficient MoE inference pipeline. Extensive experiments demonstrate the effectiveness of our methods, showing significant improvements in inference efficiency, e.g., 0.2\% average performance increase and a 1.94times inference speedup on Mixtral-8times7B-Instruct.