MoX: Efficient MoE Routing on Direct-Connect Topologies

Optically switched networks suit the regular communication of dense ML models, but MoE introduces sparse, runtime-dependent traffic. We show that efficient offline-optimized routing enables efficient MoE training and inference on direct-connect topologies without the need for MoE traffic matrix or dynamic topology reconfiguration. MoX constructs token-aware multicast trees to reduce bandwidth tax, then uses static, precomputed link weights to balance traffic by solving a restricted multicast tree-packing problem. Using recorded traffic from large MoE models, token-level traces, and ASTRA-sim, we find that MoX accelerates the full MoE block -- dispatch, expert computation, and combine -- by up to 1.8x over min-hop routing. Moreover, it attains nearly ideal packet-switched network performance in random expander topologies. On a 1,024-TPU model of Google's Boardfly topology, MoX reduces the dispatch bottleneck link load by up to 47%. These results show that high-performance MoE on static direct-connect fabrics can be achieved via optimized load-oblivious routing without demand-driven reconfiguration.