Resilient model training on Red Hat OpenShift AI with Kubeflow Trainer

Source: Red Hat Blog

Overview

Imagine that after 60 hours of training, a large language model (LLM) on an 8× 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…