I knew the surface level stuff, like experts are subnetworks and only some get activated per input.
Start by defining the router's role as a learned gating function that assigns each input token to a subset of experts. Then explain the routing mechanism (e.g., top-k gating with softmax) and discuss trade-offs like load balancing and capacity factor. Finally, connect it to system design considerations such as scalability and efficiency.
Pro tip: Emphasize that the router is trained jointly with experts and that its design directly impacts model quality and serving cost—showing you understand the end-to-end implications beyond just the algorithm.
Explain that the router is a learned gating network that decides which experts process each input token, enabling conditional computation.
Detail how the router computes logits, applies softmax, and selects top-k experts (typically k=1 or 2) per token, often with a noisy top-k gating for exploration.
Mention that the router is trained end-to-end with a load balancing loss to prevent expert underutilization and ensure even token distribution.
Talk about capacity factor, token dropping, communication overhead in distributed settings, and how routing affects latency and throughput.
Relate to scalability, cost-efficiency, and fault tolerance in large-scale systems, highlighting why MoE is attractive for serving massive models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.