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Deploy and evaluate models with Amazon SageMaker JumpStart

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Deploy and evaluate models with Amazon SageMaker JumpStart

Choosing the right model for a specific application requires a practical way to test it with representative inputs. Amazon SageMaker JumpStart provides access to a broad catalog of already pretrained models. In this post, we show how to deploy an available model from SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete. For supported models and available capacity, this workflow provides a repeatable evaluation path, although deployment time varies by model, instance type, quota, and AWS Region.

In 2026, SageMaker JumpStart introduced two improvements that simplify model discovery and deployment. In the Model Hub section of the Amazon SageMaker AI console, you can browse the SageMaker JumpStart catalog, view sample notebooks, and open them directly in SageMaker Studio. Pre-configured endpoint deployment options provide named configurations optimized for priorities such as low cost, low latency, or general-purpose workloads across a range of generative AI models, including Neuron-optimized configurations. Additionally, you can now go from Hugging Face model discovery to SageMaker AI deployment in a single click using the new deep link integration.

This pattern works for many compatible SageMaker JumpStart models, although payload formats, required instance types, end-user license agreement (EULA) requirements, and response schemas may require model-specific adjustments.

Figure 1: Browsing the model catalog on the SageMaker JumpStart Models page in SageMaker Studio

Figure 2: Deploying a model from its SageMaker JumpStart detail page in SageMaker Studio

This pattern works for many available SageMaker JumpStart models, but the payload format, required instance type, EULA, and response parsing may require model-specific adjustments.


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