MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference
Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP) inference due to the straggler effect. This issue is worsened in the multimodal context, as existing token-count-based load balancing methods fail to address two unique challenges: (1) Information Heterogeneity, where numerous redundant visual tokens are treated equally to semantically critical ones, and (2) Modality Dynamics, where varying visual to text ratios across tasks lead to resource misallocation. To address these challenges, we propose MACS (Modality-Aware Capacity Scaling), a training-free inference framework. Specifically, MACS introduc...
| Authors | Bo Li, Chuan Wu, Shaolin Zhu |
|---|---|
| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 5 June 2026 |
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