HyperPod now supports Multi-Instance GPU to maximize GPU utilization for generative AI tasks

This article announces general availability of GPU partitioning with Amazon SageMaker HyperPod using NVIDIA Multi-Instance GPU (MIG) technology.

• Run multiple concurrent tasks on single GPU, minimizing wasted compute and memory resources
• MIG partitions GPUs into isolated instances with dedicated memory, cache, and compute cores
• Supports flexible resource allocation across teams with predictable performance and workload isolation
• Two setup experiences: managed MIG (recommended, instance group level) and DIY (Kubernetes labels)
• Compatible with ml.p5en.48xlarge and other supported GPU instances with Ampere/Hopper/Blackwell architectures
• Integrates with HyperPod features: task governance, observability dashboards, autoscaling, inference operator
• Practical use cases: resource-guided model serving, mixed workloads, development/testing efficiency
• Hands-on examples demonstrate concurrent inference, disaggregated inference, and interactive Jupyter notebooks
• Includes comprehensive monitoring, quota management, and enterprise-grade reliability features

MIG on SageMaker HyperPod enables organizations to maximize GPU infrastructure investment through flexible partitioning, cost optimization, and efficient resource sharing across teams and workloads.