AWS outlines multi-team GPU sharing for SageMaker HyperPod with isolation controls
AWS’s Machine Learning Blog detailed a reference architecture for Amazon SageMaker HyperPod to enable multiple teams within a company to share GPU clusters while enforcing isolation, fairness, and cost attribution. The setup uses Amazon EKS for cluster orchestration, AWS IAM Identity Center for centralized authentication, and Kubernetes namespaces to segment workloads by team. Each team gets its…
Key points
- AWS’s SageMaker HyperPod reference architecture lets multiple teams share GPU clusters with per-team isolation via Kubernetes namespaces and IAM roles
- Each team gets its own SageMaker AI domain, compute quotas, and storage (FSx/Lustre or S3) with IAM-scoped access to prevent overuse or interference
- Observability is restricted to team-specific Grafana dashboards, and workloads (training/inference) are namespace-scoped to enforce boundaries
Key components include EKS access entries mapping IAM roles to Kubernetes permissions, NetworkPolicies to restrict pod-to-pod traffic, and HyperPod Training/Inference Operators for namespace-scoped jobs. The blog emphasizes this is designed for trusted internal teams—not for multi-cloud or adversarial isolation—highlighting that namespaces alone don’t prevent determined malicious actors from accessing shared nodes. The solution aims to balance operational simplicity with accountability, enabling teams to run distributed training, inference, and development workloads without administrative overhead.
The story so far
4 episodes →- AWS outlines multi-team GPU sharing for SageMaker HyperPod with isolation controlsthis story
Share GPU clusters across teams with isolation and fairness using Amazon SageMaker HyperPod
AWS Machine Learning Blog · 8 October 2026
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This text was published by AWS Machine Learning Blog and written by Giuseppe Angelo Porcelli. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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2sources- Mixture-of-Experts kernel engineering on Amazon SageMaker AIPrimary source · AWS Machine Learning Blog ·
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