SageMaker Studio IDEs support GPU capacity reservation
Amazon SageMaker Studio IDEs now support GPU capacity reservations via SageMaker Flexible Training Plans (FTP). This provides predictable access to high-performance GPU resources with potential cost savings of up to 65% compared to On-Demand instances. The feature is available for users running ML workflows in JupyterLab or Code Editor within Studio, offering a self-serve procurement experience.
- →GPU capacity reservation for SageMaker Studio IDEs
- →Cost savings with Flexible Training Plans
- →Simplified procurement and provisioning
- →Proactive expiration notifications
Features (1) ›
- GPU capacity reservation for SageMaker Studio IDEs
SageMaker Studio IDEs, including JupyterLab and Code Editor, now support GPU capacity reservations using SageMaker Flexible Training Plans. This aims to provide predictable access to GPU resources, potentially offering significant cost savings for ML workflows.
Enhancements (2) ›
- Cost savings with Flexible Training Plans
Leveraging Flexible Training Plans can achieve up to 65% cost savings compared to On-Demand instances for ML workflows run within SageMaker Studio IDEs. This is facilitated through a fully self-serve procurement process for GPU capacity.
- Proactive expiration notifications
The IDE proactively notifies users as their GPU capacity reservation plan nears expiration. This allows users to save their work before the reservation ends, ensuring continuity for ongoing ML tasks.
Maintenance (1) ›
- Simplified procurement and provisioning
Users can procure GPU capacity by navigating to the SageMaker FTP console, selecting instance type, reservation length, and start date. SageMaker then automatically provisions the instance when creating a Studio app, requiring no infrastructure management.
https://aws.amazon.com/about-aws/whats-new/2026/05/amazon-sagemaker-training-plan-support-for-studio/
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