AWS Machine Learning Blog· Bryan Berezdivin·· 2026-09-25
Multi-Region training with Amazon SageMaker HyperPod and Qumulo
Multi-Region training with Amazon SageMaker HyperPod and Qumulo
AI summary
Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another. This post shares the architecture and validation results from a cross-Region training run, where a remote cluster matched a co-located cluster's throughput after a brief NeuralCache warmup.
Selection record
Threshold 60Official, first-handFirst 31Second 34
Not admittedSum of both 65 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：多区域训练大AI模型，含架构与验证
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com