Organizations are racing to deploy generative AI models into production to power intelligent assistants, code generation tools, content engines, and customer-facing applications. But deploying these models to production remains a weeks-long process of navigating GPU configurations, optimization techniques, and manual benchmarking, delaying the value these models are built to deliver.

Today, Amazon SageMaker AI supports optimized generative AI inference recommendations. By delivering validated, optimal deployment configurations with performance metrics, Amazon SageMaker AI keeps your model developers focused on building accurate models, not managing infrastructure.

We evaluated several benchmarking tools and chose NVIDIA AIPerf, a modular component of NVIDIA Dynamo, because it exposes detailed, consistent metrics and supports diverse workloads out of the box. Its CLI, concurrency controls, and dataset options give us the flexibility to iterate quickly and test across different scenarios with minimal setup.

“With the integration of modular components of the open source NVIDIA Dynamo distributed inference framework directly into Amazon SageMaker AI, AWS is making it easier for enterprises to deploy generative AI models with confidence. AWS has been instrumental in advancing AIPerf through deep collaboration and technical contributions. The integration of NVIDIA AIPerf demonstrates how standardized benchmarking can eliminate weeks of manual testing and deliver validated, deployment-ready configurations to end users.”

Deploying models at scale requires production inference endpoints that satisfy clear performance goals, whether that is a latency service level agreement (SLA), a throughput target, or a cost ceiling. Achieving that requires finding the right combination of GPU instance type, serving container, parallelism strategy, and optimization techniques, all tuned to the specific model and traffic patterns.

Figure 1: The three core challenges teams face when deploying generative AI models to production