FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving
Large-scale expert parallelism (EP) has become the standard deployment configuration for Mixture-of-Experts (MoE) models in production. Meanwhile, the highly dynamic nature of LLM workloads requires providers to continuously adjust serving capacity to match fluctuating demand. Scaling MoE serving cannot be efficiently achieved by simply adding or removing serving instances; instead, it often requires resizing the EP group within an existing instance, which we refer to as intra-instance scaling.Existing inference engines primarily rely on instance-level scaling, which destroys and reconstructs entire serving instances, incurring substantial scaling overhead. We present FastEEP (Fast Elasti...
| Authors | Yunfei Feng, Jingkai He, Shiqi Liu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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