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English(EN) Specter: Fast MoE Inference via Quantization-Enabled Multi-Step Speculative Offloading

Specter 系统通过量化和推测卸载加速 MoE 推理

研究人员开发了 Specter,一个旨在加速专家混合(MoE)模型的新型推理系统,这些模型通常会超出单个加速器的内存容量。Specter 结合了量化、推测解码和专家卸载,以克服数据依赖路由引起的 PCIe I/O 瓶颈。通过使用轻量级的量化草稿模型来预测未来的专家需求,Specter 能够主动组织专家的移动和执行,从而提高通信效率。这种方法平均将每个输出令牌的时间速度提高了 1.50 倍,使得在单 GPU 系统上进行 MoE 推理更加可行。 AI

影响 使得在通用硬件上对大型 MoE 模型进行更高效、更实用的推理成为可能。

排序理由 该集群描述了一篇详细介绍 MoE 模型新推理系统的研究论文。

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Specter 系统通过量化和推测卸载加速 MoE 推理

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Wenfeng Wang, Xiaofeng Hou, Xinfeng Xia, Xinkai WAng, Mingxuan Zhang, Peng Tang, Jiacheng Liu, Chao Li ·

    Specter:通过量化实现多步推测卸载,实现快速 MoE 推理

    arXiv:2511.14102v2 Announce Type: replace Abstract: Mixture-of-experts (MoE) models often exceed a single accelerator's memory capacity. Offloading experts to host memory creates a severe PCIe I/O bottleneck because data-dependent routing keeps transfers on the critical path. We …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Specter:通过量化多步推测卸载实现快速 MoE 推理

    Specter accelerates single-GPU mixture-of-experts inference by combining quantized speculative decoding with expert offloading to hide PCIe latency and improve throughput.