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Shared GPU clusters need tenant isolation, fair allocation and cost attribution

aws.amazon.com by Giuseppe Angelo Porcelli 26 min read Editor score 6/10

The guide addresses sharing GPU clusters across teams running training, inference, and experimentation while preserving isolation, fairness, and operational independence. These are central trade-offs when building multi-tenant AI infrastructure.

What: SageMaker HyperPod’s reference architecture lets multiple teams share one EKS cluster with isolated namespaces, fair allocation, and per-team cost visibility.
How: IAM Identity Center authenticates users; permission sets support CLI access, while team SageMaker domains and Kubernetes namespaces isolate workflows.
Why it matters: HyperPod Task Governance and namespace-level cost allocation address fair GPU sharing and chargeback for AI workloads.

The CLI path authenticates users, grants team credentials, and submits work to isolated cluster namespaces. Sketched from the article's own text.

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