Best practices to run inference on Amazon SageMaker HyperPod

This article provides best practices for running inference on Amazon SageMaker HyperPod, a managed platform for deploying generative AI models at scale.

• One-click cluster creation with Amazon EKS orchestration simplifies deployment setup
• Flexible deployment options from SageMaker JumpStart, S3, and FSx for Lustre without coding
• Dual-layer autoscaling: KEDA for pod-level and Karpenter for node-level scaling
• Scale-to-zero capability eliminates costs during idle periods with no autoscaler overhead
• Managed tiered KV cache reduces GPU memory pressure and supports longer context windows
• Intelligent routing maximizes cache reuse for multi-turn conversations and batch requests
• Up to 40% latency reduction, 25% throughput improvement, 25% cost savings with optimizations
• Multi-Instance GPU (MIG) support enables efficient small model deployment on large GPUs
• Built-in observability dashboards in Grafana for monitoring inference metrics
• Support for interactive development environments like JupyterLab on HyperPod clusters

SageMaker HyperPod enables organizations to deploy foundation models efficiently with automated infrastructure, intelligent resource management, and significant cost reductions while accelerating time-to-market.