How Do I Run AI Workloads on Kubernetes Without Wasting GPUs? (opens in new tab)

Many teams want to run AI and ML workloads on Kubernetes but are worried about wasting GPUs, overcomplicating the platform, or breaking reliability for the rest of their services. The good news is that Kubernetes can work very well for model training, batch jobs, and real time inference, including LLM APIs and vector search services, as long as you plan for GPU scheduling, right size your workloads, and put guardrails in place so expensive nodes and jobs don’t run unchecked.