N Load‑Balancing Techniques for Large‑Scale MoE Models

The article explains why token‑to‑expert routing in large Mixture‑of‑Experts models can become highly imbalanced, outlines the resulting compute bottlenecks, token overflow and expert degradation, and then surveys eight concrete load‑balancing strategies—ranging from importance‑based auxiliary losses to capacity‑aware token dropping and loss‑free bias control—detailing their mechanisms, trade‑offs, and practical considerations.

Auxiliary LossBalanced AssignmentCapacity Factor

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N Load‑Balancing Techniques for Large‑Scale MoE Models