English(EN) Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

AWS SageMaker HyperPod 通过模型缓存减少 LLM 推理冷启动

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 21:37

Amazon SageMaker HyperPod 引入了模型缓存功能，以显著减少推理冷启动时间。此新功能将模型权重和容器镜像预加载到集群节点上，使 Pod 能够从本地 NVMe 存储高速访问数据，而不是通过网络下载。以前，像 DeepSeek-R1 这样的大型模型可能需要 30 多分钟才能准备好进行推理，但通过模型缓存，Pod 通常可以在几秒钟内开始提供流量。 AI

影响降低 AWS 上 LLM 部署的运营成本并提高响应速度。

排序理由这是对现有云 ML 平台的特性更新，不是新的前沿模型发布或重大的行业性事件。

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AWS SageMaker HyperPod 通过模型缓存减少 LLM 推理冷启动

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AWS Machine Learning Blog TIER_1 English(EN) · Kareem Syed-Mohammed · 2026-09-10 21:37

Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to…

本文如何被排名

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Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

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