Training state-of-the-art models and large fine-tunes frequently stall on two issues: GPU availability and infrastructure complexity. In this 40-minute, practitioner-level session, I’ll break down the fastest and most cost-effective paths to dedicated H100/B200-class GPU capacity on AWS, and how to translate that into repeatable distributed training runs. We’ll compare On-Demand, Spot, Savings Plans, Capacity Blocks, and SageMaker Training Plans with HyperPod, with a decision framework for research, production training, and massive fine-tunes. I’ll then walk through a hands-on setup using PyTorch + DeepSpeed, showing how to provision a multi-node cluster, configure distributed training, and implement checkpointing and recovery so runs survive interruptions and node failures. Attendees will leave with reference Terraform/CDK patterns, reliability guardrails, and cost tactics they can apply immediately to reduce wasted compute and missed training timelines.
Technical Level: Technical practitioner