Why is Mixture of Experts (MoE) becoming standard in fr...

A dense model activates every parameter for every token, so total parameters and per-token compute are the same number — to get more capacity, you pay for more compute on every single token, whether that token needed it or not.

Mixture of Experts decouples those two things. A learned router picks a small subset of “expert” feed-forward blocks to activate for each token, out of a much larger pool. Total parameter count can be many times larger than what’s actually used per token, so you get more capacity for the same per-token inference cost as a much smaller dense model.

The cost isn’t free: you need to hold all the experts in memory even though only a few run per token, which is a large memory footprint relative to active compute. And the router itself is a real engineering problem — load balancing across experts, routing instability during training, and expert-parallel communication overhead in distributed training are all genuinely harder than anything in a dense model.