MMOE: Modernizing Diffusion Transformers with Efficient Expert Design
Modern large language models scale successfully by pairing capacity growth with efficiency, keeping per-token and deployment costs under control as capacity grows. AIGC Foundation Models (AFMs), especially diffusion-transformer backbones, have begun to adopt sparse experts, but recent efforts mostly enlarge total parameter counts and sparsity ratios without importing the efficiency mechanisms that made LLM scaling practical, so generation quality is seldom balanced against training and deployment cost. This raises a natural question: can the architectural principles behind efficient LLM scaling be adapted to AFMs in a more balanced way? We introduce ModernMOE (MMOE), a modernization of Si...
| Authors | Yanhao Jia, Jiepeng Wang, Haibin Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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