Share GPU clusters across teams with isolation and fairness using Amazon SageMaker HyperPod

The article outlines a reference architecture that lets multiple teams share a single Amazon SageMaker HyperPod EKS cluster while maintaining strict isolation and fairness. By leveraging AWS IAM Identity Center for authentication, assigning each team its own SageMaker Domain and Kubernetes namespace, and enabling HyperPod Task Governance, the design guarantees that workloads are isolated, scheduled fairly, and billed accurately at the namespace level. This approach reduces cluster fragmentation and simplifies cost tracking for large multi‑tenant AI workloads.

⚡ Key Takeaways

• A single SageMaker HyperPod EKS cluster can be partitioned across teams using per‑team SageMaker Domains and Kubernetes namespaces.
• AWS IAM Identity Center is used to authenticate users and map them to the correct domain and namespace.
• HyperPod Task Governance enforces workload fairness and prevents any single team from monopolizing GPU resources.
• Namespace‑level cost allocation tags automatically attribute GPU usage to the owning

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