With Amazon SageMaker HyperPod and Qumulo, you can place training compute in one AWS Region and keep your dataset in another. Training large AI models requires massive GPU capacity, but your ideal compute resources and your training data don’t always reside in the same AWS Region. Accessing data across Regions adds network latency and transfer costs. Teams face a choice: either replicate petabytes of data across Regions, or absorb cross-Region latency on every read and accept slower training. This pairing can help tackle that trade-off, letting teams keep frontier models current without moving data or sacrificing throughput.
In this post, we present a solution to this challenge, explain the architecture, and share validation results from a cross-Region training run. By pairing Amazon SageMaker HyperPod with Cloud Native Qumulo (CNQ) and Qumulo’s Cloud Data Fabric (CDF), training jobs can read datasets stored in another AWS Region or on-premises without copying the data, changing the code, or sacrificing throughput. We validated the approach by running the same training job independently on two clusters. The hub cluster runs in the US East (Ohio) Region (us-east-2), co-located with the data. The spoke cluster runs in the US West (Oregon) Region (us-west-2) and reads the data remotely through CDF with 60 ms of network latency. We then compared their throughput. After a short warmup, the spoke cluster matched the hub’s performance.
Amazon SageMaker HyperPod provides the managed training infrastructure including resilient clusters with automatic health checks, node replacement, and checkpoint recovery. The solution pairs SageMaker HyperPod with Qumulo’s storage layer.
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