Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans

This article explains how to secure GPU capacity for short-term ML workloads using EC2 Capacity Blocks for ML and SageMaker training plans.

• GPU demand exceeds supply; short-term capacity reservations address this challenge
• On-demand instances offer flexibility but uncertain availability; no cost advantage
• Spot instances reduce costs up to 90% but risk interruption; suitable for checkpointable workloads
• EC2 Capacity Blocks reserve GPU capacity for 1-182 days with 40-50% discount; supports up to 256 instances
• SageMaker training plans offer 70-75% discount for managed ML workloads; covers training, HyperPod, inference
• Decision framework: start with on-demand, use reserved capacity when timing certainty required
• Step-by-step guide provided for creating SageMaker training plans for inference endpoints

Choose reserved capacity options when workload timing is critical; otherwise start with on-demand for cost flexibility.