Sparse Mixture-of-Experts (MoE) Gating is the routing mechanism that selects which expert networks process each token in an MoE model — enabling scaling to trillions of parameters while keeping per-token computation constant.

MoE Architecture Overview

• Replace each FFN layer with E parallel expert networks.
• For each token, a gating network selects the top-K experts.
• Only K experts compute the output — rest are inactive.
• Parameter count scales with E; compute scales with K (not E).

Gating Mechanism

• $W_g$: learned routing weight matrix.
• Top-K: Keep only the K highest scores, zero the rest.
• Weighted sum of selected expert outputs.

Load Balancing Problem

• Without regularization, the router collapses — all tokens go to a few popular experts.
• Other experts get no gradient signal and become useless.
• Solution: Auxiliary Load Balancing Loss — penalize imbalanced routing:

$L_{aux} = \alpha \sum_e f_e \cdot p_e$ where $f_e$ = fraction of tokens routed to expert $e$, $p_e$ = mean gating probability.

Expert Capacity

• Each expert has a fixed capacity (max tokens per batch).
• Overflow tokens are dropped or passed through a residual connection.
• Capacity factor CF=1.0: No slack; CF=1.25: 25% headroom.

MoE Routing Variants

• Top-1 Routing (Switch Transformer): Single expert per token — simpler, load issues.
• Top-2 Routing (GShard, Mixtral): Two experts — better quality, manageable overhead.
• Expert Choice (Zoph et al., 2022): Experts choose tokens rather than tokens choosing experts — perfect load balance.
• Soft Routing: All experts compute, weighted combination (expensive but no dropped tokens).

Production MoE Models

MoE gating is the key to scaling LLMs beyond the memory/compute frontier — it decouples parameter count from inference cost, enabling trillion-parameter models at 7B-class inference cost.