Neural network architectures

MoE Routing

MoE routing is the gating mechanism that decides which experts a token activates in a Mixture-of-Experts layer. Top-k routing (each token picks its k highest-scoring experts) is dominant — Mixtral and DeepSeek use top-2 and top-8 respectively.

Routing quality depends on training: untrained or poorly-trained routers cluster tokens onto a few experts (load imbalance), wasting capacity. Auxiliary load-balancing losses during training fix this.

At inference, routing introduces an all-to-all communication step in distributed setups; on a single GPU, it's a sparse gather. Quantizing experts independently is harder than quantizing dense weights — different experts have different activation distributions.

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