Deploy and evaluate models with Amazon SageMaker JumpStart

Amazon SageMaker JumpStart has introduced two improvements in 2026 that simplify model discovery and deployment. The Model Hub section of the Amazon SageMaker AI console lets users browse the 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, a new deep link integration allows users to go from Hugging Face model discovery to SageMaker AI deployment in a single click. The post demonstrates a reusable Python script that performs three steps: deploy an available model from JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete. This workflow offers a repeatable evaluation path for supported models, though deployment time varies by model, instance type, quota, and AWS Region. By using consistent test prompts and measuring latency, throughput, and output quality across models, users can compare options and select the right fit for their workload. The approach works for many compatible JumpStart models, but payload formats, required instance types, EULA requirements, and response schemas may require model‑specific adjustments.