AWS Releases Amazon SageMaker HyperPod: Efficient Architecture for Multi-Team GPU Cluster Sharing

Summary:AWS has launched Amazon SageMaker HyperPod, a reference architecture designed for multi-team sharing of GPU clusters. This architecture leverages AWS IAM Identity Center for authentication, SageMaker AI domains and Kubernetes namespaces for team isolation, and HyperPod Task Governance to ensure fair resource allocation. Additionally, it supports namespace-level cost allocation, providing teams with transparent visibility into GPU usage costs.

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Overview of the Core Architecture

AWS has recently introduced Amazon SageMaker HyperPod, a reference architecture for multi-team sharing of GPU clusters, addressing the challenges of resource isolation, fair allocation, and cost attribution faced by enterprises in their generative AI operations. The key components of this architecture are as follows:

1. User Identity and Authentication

• AWS IAM Identity Center: Serves as the centralized authentication layer, integrating with external identity providers (e.g., Microsoft Entra ID) to manage user identities and group memberships.
• Identity Federation: Seamlessly integrates with existing enterprise identity management systems, avoiding the duplication of user accounts.

2. SageMaker AI Domains

• Team Isolation: Each team is assigned a dedicated SageMaker AI domain, providing a customized user interface and execution role, ensuring operational independence and data isolation between teams.
• Single Sign-On (SSO): Users sign in through the Identity Center and are directly routed to their team's SageMaker Studio environment.

3. EKS Access Control

• Kubernetes RBAC: EKS access entries map IAM roles to Kubernetes permissions, ensuring users can only access resources within their team's namespace.
• Namespace Isolation: Each team is allocated a dedicated Kubernetes namespace to isolate their workloads (e.g., training jobs, inference endpoints).

4. HyperPod Task Governance

• Resource Quota Management: Defines GPU and CPU usage quotas for each team to prevent resource monopolization by a single team.
• Scheduling Priorities: Allocates resources based on task type and team priority, ensuring critical tasks (e.g., production inference) receive resources first.

5. Storage Solutions

• POSIX File Systems: Uses Amazon FSx for Lustre or OpenZFS to provide high-performance shared storage for teams, supporting distributed training and model checkpoints.
• Object Storage: Leverages Amazon S3 for object storage, with teams accessing their dedicated buckets or using team-specific prefixes for isolation via IAM roles.

6. Cost Allocation and Monitoring

• Kubecost Integration: Implements namespace-level cost allocation through Kubecost, providing teams with detailed GPU, CPU, memory, and network usage reports, supporting internal cost sharing and budget management.
• Amazon Managed Grafana: Offers teams read-only access to monitoring dashboards, allowing them to view the performance and resource consumption of their workloads in real-time.

Technical Highlights

• Multi-Team Isolation and Collaboration: Achieves strict isolation between teams through namespaces and RBAC mechanisms while supporting efficient collaboration.
• Fair Resource Allocation: The HyperPod Task Governance mechanism ensures fair resource allocation, preventing resource monopolization and excessive competition.
• Transparent Cost Management: Namespace-level cost allocation provides teams with a clear view of their GPU usage costs, enabling detailed budget management and cost control.

Industry Impact and Developer Recommendations

• Accelerator for Enterprise AI Deployment: This architecture offers an efficient, scalable AI infrastructure solution for enterprises requiring multi-team collaboration, significantly reducing management complexity and operational costs.
• Resource Optimization and Cost Control: Developers should leverage the task governance and cost allocation features of HyperPod to optimize resource usage and control costs.
• Security and Compliance: It is recommended that enterprises combine IAM policies and Kubernetes RBAC to ensure data security and compliance.

Conclusion

Amazon SageMaker HyperPod provides a comprehensive and flexible solution for multi-team sharing of GPU clusters, combining key features such as identity authentication, team isolation, resource governance, and cost allocation. This architecture is not only applicable to existing EKS clusters but can also be extended to other orchestration backends, laying a solid foundation for the modernization of enterprise AI infrastructure.

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Source: AWS Machine Learning Blog (2026-10-08)

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Tags: #AWS Follow #SageMaker Follow #Multi-Team Collaboration Follow #GPU Clusters Follow #Resource Governance Follow