Imagine that after 60 hours of training, a large language model (LLM) on an 8x NVIDIA H100 GPU cluster costing $55 an hour, your job fails at 90% completion. You must restart from your last checkpoint, which was saved 3 hours ago, wasting $165 in compute costs, and delaying model deployment. This kind of scenario isn’t hypothetical, it’s a daily reality for organizations running distributed AI training workloads in production environments. LLM training represents one of the most compute-intensive workloads in modern AI infrastructure. With GPU clusters costing thousands of dollars and training
