A media analytics company runs large distributed deep-learning training jobs on a fleet of GPU instances. Each job runs for 12-18 hours and can be resumed from periodic checkpoints written to shared storage. The training framework already supports node failures by re-launching workers. Management wants to reduce compute cost by at least 60% without extending total training time significantly, and the solution must tolerate individual node terminations gracefully. Which approach best meets these requirements?