Amazon SageMaker Studio now supports GPU capacity reservation through SageMaker Flexible Training Plans

Amazon SageMaker Studio IDEs, including JupyterLab and Code Editor, now support GPU capacity reservations through [SageMaker Flexible Training Plans (FTP)](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html), giving you predictable access to high-demand, high-performance computational resources within your budget. By leveraging FTP, you can achieve up to 65% cost savings compared to On-Demand instances while running ML workflows in JupyterLab or Code Editor. FTP provides a fully self-serve procurement experience. To get started, navigate to the SageMaker FTP console and select your preferred instance type, reservation length, and start date for your Studio IDE workload. Review your order, complete the purchase, and wait for the plan to become active. When creating a Studio app from the SageMaker Studio UI, select your purchased plan from the Instance dropdown. SageMaker provisions the instance automatically with no infrastructure management required on your part. As your plan nears expiration, the IDE proactively notifies you, giving you time to save your work before the reservation ends. To learn more about using FTP capacity reservation capability with Studio IDEs, see [Using Training Plans with Studio IDEs](https://docs.aws.amazon.com/sagemaker/latest/dg/training-plan-utilization-for-studio-apps.html). To learn about launching JupyterLab and Code Editor applications in SageMaker Studio, see [Studio Spaces documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-spaces.html).