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English(EN) FastAFD: When Does Attention-FFN Disaggregation Pay on Rack-Scale NVLink?

FastAFD引擎在NVLink系统上提升MoE模型服务吞吐量

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文介绍FastAFD，这是一种旨在提高在GB200 NVL72等机架规模NVLink系统上服务大型混合专家（MoE）模型效率的引擎。FastAFD将注意力与专家分离，允许它们并发处理，与标准的vLLM配置相比，Qwen3和Qwen3.5等模型的吞吐量提高了1.17倍。该论文还提出了一个分析模型来预测AFD的性能优势，表明当更大批次大小和改进的重叠带来的收益超过分离成本时，AFD最具优势。 AI

影响 优化大型MoE模型的服务基础设施，可能降低AI应用的延迟并提高吞吐量。

排序理由 研究论文，详细介绍了一种优化AI模型服务基础设施的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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FastAFD引擎在NVLink系统上提升MoE模型服务吞吐量

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yichao Fu, Yuxuan Zhang, Minshen Zhang, Ruitian Wang, Junda Chen, Hao Zhang ·

    FastAFD：当注意力-FFN分离在机架规模NVLink上奏效时？

    arXiv:2611.07149v1 Announce Type: cross Abstract: Attention-FFN disaggregation (AFD) separates attention from the experts of a mixture-of-experts (MoE) model and has been reported to raise decode throughput over colocated serving. Whether this advantage holds on rack-scale NVLink…

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