For MoE inference, the worst enemy isn't model size — it's uneven expert routing: a few experts get crushed, the others sit idle, and the whole batch is dragged down by "the slowest one", a phenomenon the paper calls the Straggler Effect. Shwai He et al. from UMD formalized this and, at ICLR 2026, proposed Capacity-Aware Inference: first, Capacity-Aware Token Drop sets a per-expert capacity ceiling, dropping overflow tokens from overloaded experts for up to 30% speedup (only a 0.9-point drop on OLMoE); then Expanded Drop, before dropping, reroutes tokens to a less-loaded alternate expert on the same card, achieving 1.85x inference speedup on Mixtral-8x7B-Instruct while actually gaining 0.2 points on average. Crucially, this is a purely inference-time method: it doesn't touch the weights, doesn't retrain, and can be dropped onto any existing MoE checkpoint with apply_capacity_aware_moe_patch — so for sparse models already in production like OpenMoE / DeepSeek-V3 / Mixtral, it's free money. The code is open-sourced (case-lab-umd/Capacity-Aware-MoE, 20 stars, 90 commits), and it has been wired into both lm-evaluation-harness and VLMEvalKit, so it can be validated on pure-text and multimodal MoE alike. The lesson for engineering teams is clear: when optimizing MoE inference, "uneven expert routing" is usually a bigger lever than "experts are too sparsely activated" — the former is the wooden barrel's short plank and directly determines P99 latency. arXiv:2503.05066v5, accepted at ICLR 2026.