load balancing

MoE trims compute but is awkward to serve: experts have to be sharded, tokens routed across devices, and batches balanced. What matters is the all-to-all communication and the load-imbalance problem, not the training story.

Looks simple until the follow-ups: which layer do you balance at, how do you keep traffic off a dead server, and how does the load balancer itself avoid being a single point of failure? Interviewers want the algorithm, the health-check loop, and the high-availability story.

Round-robin routing throws away a feature that doubles throughput: cache locality. Designing the router means picking a replica by KV state, not just least-loaded. Here is the two-layer design.

MoE gives you more parameters for the same FLOPs, but the gating network, the load imbalance, and the all-to-all shuffle bring failure modes dense models never see. Here is the training playbook.