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AWS published a reference architecture for sharing a single SageMaker HyperPod GPU cluster across multiple teams with security isolation, fair scheduling, and cost allocation, reducing AI infrastructure waste in enterprises.

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AWS Launches Reference Architecture for Sharing GPU Clusters Across Teams on SageMaker HyperPod

What did AWS announce?

FAQ

What is Amazon SageMaker HyperPod?

An AWS service for building and managing purpose-built compute clusters for AI and ML workloads on Kubernetes, optimized for large-scale GPU usage.

How does the architecture achieve team isolation?

It uses AWS IAM Identity Center for authentication, separate SageMaker Domains and Kubernetes namespaces per team, and HyperPod Task Governance for fairness, with namespace-level cost allocation.

Is this relevant for MENA organizations?

Yes. Enterprises and government entities running shared GPU clusters can reduce waste, improve internal chargeback, and enforce clear security boundaries between teams.

Source: AWS Machine Learning

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