Every Mixture-of-Experts (MoE) model repeats one decision for every token: which experts should process it. That decision belongs to the router. MoE routing strategies shape quality, load balance and serving cost in every major open model. This guide explains the 7 routing strategies used in production, and the models that use each one.
MoE routing is the mechanism that sends each token to a small subset of expert networks. A router scores every expert, keeps the top candidates and weights their outputs. Only the selected experts run. Mixtral 8x7B, for example, gives each token access to 47B parameters but uses 13B active parameters.
Each token scores all experts with a linear layer and keeps the k highest. A softmax over those k scores sets the mixing weights. Mixtral picks 2 of 8 experts. gpt-oss-120b picks 4 of 128. Qwen3-235B-A22B picks 8 of 128. An auxiliary loss usually keeps expert load even.
Switch Transformer showed that routing each token to 1 expert preserves quality and cuts router compute. Each expert gets a fixed buffer: tokens per batch divided by experts, times a capacity factor. Overflow tokens skip the layer through the residual connection. Llama 4 Maverick also sends each token to 1 routed expert, plus a shared expert.
Source: MarkTechPost — Read the full article on the original site ↗