English(EN) Threadripper PRO CPU experts offload numbers

CPU核心对于MoE卸载来说不如内存带宽重要

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 15:40

Reddit的r/LocalLLaMA论坛的一位用户进行了实验，以确定卸载超出GPU VRAM的大型MoE模型的最佳CPU配置。在AMD Ryzen Threadripper PRO 9975WX上进行的测试表明，超过24个CPU核心对于MoE卸载的收益递减，内存带宽和散热是主要的瓶颈。观察到的持续吞吐量约为150 GB/s，峰值可达186 GB/s，这表明在混合CPU-GPU方法中，内存子系统性能和热管理对于实现更高的解码速度至关重要。 AI

影响优化用于大型MoE模型的CPU-GPU混合设置，可以通过优先考虑内存带宽和散热而非核心数量来显著提高推理速度。

排序理由用户进行的关于AI模型卸载硬件配置的基准测试分析。

在 r/LocalLLaMA 阅读 →

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报道来源 [1]

r/LocalLLaMA TIER_1 English(EN) · /u/mr_il · 2026-09-10 15:40

Threadripper PRO CPU专家卸载数字

<div class="md"><p>I'm planning a PC build around a discrete GPU that would allow to run MoE models much larger than VRAM at an acceptable speed using FreeToken approach. The gist is that MoE decode of >VRAM model on GPU is PCIe bandwidth-bound, but you can gain…

本文如何被排名

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Threadripper PRO CPU专家卸载数字

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