Sparse Per Token, Dense Per Batch: XShare Reprices MoE Routing at Inference Time
TL;DR for operators Sparse MoE routing does not guarantee a sparse serving batch. As more tokens are decoded together, especially under speculative decoding, their individual top-k choices can collectively touch a large fraction of the model’s experts. That increases expert-weight movement during a memory-bound stage of inference. XShare treats this as a serving-policy problem rather than a model-retraining problem. It ranks experts using router scores from the current batch, limits the shared expert pool, and then lets each token perform its normal top-k refinement within that pool. In the reported GPT-OSS 120B experiments, moderate restrictions improve output-token throughput by roughly 6-13% in standard decoding and around 13-14% in several speculative settings while keeping accuracy comparatively close to baseline. More aggressive restriction can go much faster, but the quality loss can be large. ...