DeaMoE: Efficient MoE Structure for Fast Small-Batch Decoding
Mixture-of-Experts (MoE) models have been widely adopted in real-time interactive applications such as coding assistants, real-time audio-video interaction systems. To meet the extremely low response latency requirements of these scenarios, practitioners commonly employ small-batch decoding, under which MoE inference becomes memory-bound and is severely bottlenecked by expert weight loading. However, this bottleneck has received limited attention, and existing solutions such as post-training weight compression or fine-grained expert design during pre-training either degrade model accuracy or introduce additional computation and communication overhead. To tackle this issue, we propose DeaM...
| Authors | Zewen Jin, Shen Fu, Zeping Duan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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