Sharing Capacity Blocks for ML Across Your AWS Organization

This article explains how to share EC2 Capacity Blocks for ML across AWS Organization accounts using AWS RAM to optimize GPU utilization and reduce costs.

• Share reserved GPU capacity across teams based on actual demand, not rigid schedules
• Eliminate idle GPU resources and scheduling conflicts between ML teams
• Only standard Capacity Blocks eligible for sharing; UltraServer blocks not supported
• Enable AWS RAM sharing in management account with required IAM permissions
• Create resource shares and associate Capacity Blocks to specific accounts or OUs
• Consumer accounts automatically gain access within same AWS Organization
• Owner pays upfront reservation cost; consumers pay OS licensing and instance charges
• Monitor utilization with CloudWatch alarms and SNS email notifications
• CloudTrail logs track which accounts consume instances and when

Sharing Capacity Blocks for ML improves resource efficiency, reduces over-provisioning, and maximizes ROI on reserved GPU compute across your organization.