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id: "multi-region-training-with-amazon-sagemaker-hyperpod-and-10afee"
slug: "multi-region-training-with-amazon-sagemaker-hyperpod-and-10afee"
title: "Multi-Region training with Amazon SageMaker HyperPod and Qumulo"
description: "AWS has published an architecture and validation report for training with Amazon SageMaker HyperPod compute in one AWS Region while keeping the dataset in another through Cloud Native Qumulo."
retrieval_nugget: "AWS has published an architecture and validation report for training with Amazon SageMaker HyperPod compute in one AWS Region while keeping the dataset in another through Cloud Native Qumulo."
observed_at: "2026-09-25"
record_date: "2026-09-25"
date_kind: "observed_at"
why_it_matters: "Source: [AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/multi-region-training-with-amazon-sagemaker-hyperpod-and-qumulo)"
novelty: "notable"
verification_level: "source-linked"
signal_type: "research"
source_platform: "aws.amazon.com"
topics: []
entities: []
related_patterns: []
source_url: "https://aws.amazon.com/blogs/machine-learning/multi-region-training-with-amazon-sagemaker-hyperpod-and-qumulo"
source_urls: ["https://aws.amazon.com/blogs/machine-learning/multi-region-training-with-amazon-sagemaker-hyperpod-and-qumulo"]
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stable_id: "signal:multi-region-training-with-amazon-sagemaker-hyperpod-and-10afee"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Multi-Region training with Amazon SageMaker HyperPod and Qumulo

## Retrieval answer

AWS has published an architecture and validation report for training with Amazon SageMaker HyperPod compute in one AWS Region while keeping the dataset in another through Cloud Native Qumulo.

AWS has published an architecture and validation report for training with Amazon SageMaker HyperPod compute in one AWS Region while keeping the dataset in another through Cloud Native Qumulo. In the reported run, the remote cluster matched the throughput of a co-located cluster after a brief NeuralCache warmup. The result frames cross-Region data access as a deployable training pattern, while leaving workload-specific cost and latency validation to operators.

Source: [AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/multi-region-training-with-amazon-sagemaker-hyperpod-and-qumulo)

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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