English(EN) MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

MACS 框架提升多模态 MoE LLM 推理效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员推出 MACS，这是一个旨在提高混合专家多模态大语言模型 (MoE MLLMs) 推理效率的新框架。MACS 通过引入熵加权负载机制来更好地评估视觉 token，以及动态模态自适应容量机制来实现实时专家资源分配，从而解决了专家并行推理中的滞后效应。实验表明，MACS 在多模态基准测试上的表现显著优于现有方法，为部署 MoE MLLMs 提供了强大的解决方案。 AI

影响为 MoE MLLMs 的高效部署提供了一种新颖的解决方案，有望降低推理成本和延迟。

排序理由这是一篇详细介绍多模态模型新推理框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Bo Li, Chuan Wu, shaolin Zhu · 2026-05-08 04:00

MACS：用于高效多模态 MoE 推理的模态感知容量缩放

arXiv:2605.05225v1 Announce Type: new Abstract: 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, …

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报道来源 [1]

MACS：用于高效多模态 MoE 推理的模态感知容量缩放

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