MoEless: Efficient MoE LLM Serving with Serverless Experts
Large Language Models (LLMs) increasingly adopt Mixture-of-Experts (MoE) architectures to scale efficiently under stringent resource constraints. However, MoE's sparse activation causes severe expert load imbalance, where a few experts become stragglers while others remain underutilized, leading to inflated inference latency and cost. Existing solutions assume static, serverful model deployments, limiting expert elasticity and often incurring costly expert swapping or degraded output quality. We present MoEless, an efficient serverless MoE serving framework that mitigates expert load imbalance via elastic expert execution. MoEless leverages lightweight, layer-aware predictors to estimate ...
| Authors | Hanfei Yu, Bei Ouyang, Shwai He et al. |
|---|---|
| Primary category | cs.DC |
| Published | 6 March 2026 |
| Last updated at source | 30 September 2026 |
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