Best practices to run inference on Amazon SageMaker HyperPod

AWS has enhanced Amazon SageMaker HyperPod with comprehensive inference capabilities designed to address the operational challenges of running foundation models at scale. The platform now offers a managed inference solution that combines Kubernetes flexibility with AWS services, featuring one-click cluster creation through Amazon EKS orchestration and multiple deployment options including direct integration with SageMaker JumpStart, S3 buckets, and FSx for Lustre. This eliminates complex infrastructure setup and allows teams to deploy custom or fine-tuned models without writing code.

The most significant innovation is HyperPod's dual-layer autoscaling architecture that combines KEDA (Kubernetes Event-Driven Autoscaling) for pod-level scaling and Karpenter for node-level scaling. KEDA scales inference pods based on metrics like request queue length or CloudWatch data, while Karpenter provisions or removes compute nodes based on pending pod requirements. This integration enables true scale-to-zero capabilities—when traffic drops to zero, KEDA scales pods down and Karpenter removes all worker nodes, eliminating infrastructure costs during idle periods. The ADOT (AWS Distro for OpenTelemetry) Collector provides comprehensive monitoring to optimize this dynamic scaling.