Deploy SageMaker AI inference endpoints with set GPU capacity using training plans

📰 AWS Machine Learning

Deploy SageMaker AI inference endpoints with reserved GPU capacity using training plans

intermediate Published 24 Mar 2026

Action Steps

Search for available p-family GPU capacity
Create a training plan reservation for inference
Deploy a SageMaker AI inference endpoint on the reserved capacity
Manage the endpoint throughout the reservation lifecycle

Who Needs to Know This

Data scientists and machine learning engineers benefit from this approach as it allows them to reserve GPU capacity for model evaluation and manage inference endpoints efficiently

Key Insight

💡 Reserving GPU capacity using training plans ensures efficient model evaluation and inference endpoint management

Key Takeaways

Deploy SageMaker AI inference endpoints with reserved GPU capacity using training plans

Full Article

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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