Deploy SageMaker AI inference endpoints with set GPU capacity using training plans
Amazon AWS AI Blog / 3/25/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage
Key Points
- The article explains how to find available p-family GPU capacity and reserve it using SageMaker AI training plans for subsequent inference use.
- It walks through creating a training plan reservation tailored for inference workloads and then deploying a SageMaker AI inference endpoint that runs on the reserved GPU capacity.
- A data scientist’s end-to-end journey is used to show how to manage the endpoint across the reservation lifecycle, from reservation setup to operational handling.
- The post provides a practical workflow for aligning model evaluation and inference deployment with predictable, pre-allocated GPU resources.
In this post, we walk through how to search for available p-family GPU capacity, create a training plan reservation for inference, and deploy a SageMaker AI inference endpoint on that reserved capacity. We follow a data scientist's journey as they reserve capacity for model evaluation and manage the endpoint throughout the reservation lifecycle.
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