ExpertPlex: A High-Goodput Disaggregated Serving System for MoE LLMs with Adaptive Persistent Kernels

ExpertPlex addresses the problem of inefficient resource utilization in serving large Mixture-of-Experts (MoE) LLMs, where existing prefill-decode disaggregation wastes resources due to coarse allocation and colocation suffers from phase interference and poor load tracking. The method shares massive MoE experts across phases while disaggregating attention modules, and introduces adaptive persistent kernels, attention-initiated MoE communication, and a tile-to-cluster model to optimize execution. Experiments serving MiniMax-M2.7 and GLM-5.1-FP8 show ExpertPlex improves goodput by up to 2.01× over instance-level disaggregation and 1.66× over colocation. This matters because it enables efficient, high-throughput serving of increasingly large MoE models without overprovisioning or phase interference.