Multi-Region training with Amazon SageMaker HyperPod and Qumulo

What happened

Accessing data across Regions adds network latency (the delay between asking for something and getting it) and transfer costs. Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another.

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.

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 (how much work a system gets through in a given time).

Key facts

• 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