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English(EN) Efficient CPU-GPU collaborative MoE inference via Adaptive Expert Assignment

新系统通过 CPU-GPU 协同优化 MoE LLM 推理

一个名为 \method 的新系统已被开发出来,以提高推理过程中混合专家(MoE)大语言模型(LLM)的效率。该系统通过智能地将专家分配给 CPU 或 GPU,解决了大型专家模型内存占用超出 GPU 容量的挑战。\method 选择性地将高负载专家转移到 GPU,同时在 CPU 上执行低负载专家,动态调整分配以最小化延迟。实验表明,与现有方法相比,预填充速度和解码吞吐量有了显著提高。 AI

影响 提高了大型 MoE 模型推理的速度和效率,有望降低运营成本并改善用户体验。

排序理由 该集群描述了一篇详细介绍用于优化 AI 模型推理的新颖系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新系统通过 CPU-GPU 协同优化 MoE LLM 推理

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该集群描述了一篇详细介绍用于优化 AI 模型推理的新颖系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wenhui Lv, Yunming Liao, Hongli Xu, Yang Xu, Junhao Cheng ·

    通过自适应专家分配实现高效的 CPU-GPU 协同 MoE 推理

    arXiv:2610.23006v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models reduce inference computation through sparse expert activation, but their large expert memory footprint often exceeds GPU capacity, making expert offloading necessary for memory-constrained inference. …