Making Kubernetes AI-Ready for NVIDIA Vera Rubin on Day One

Configuring and optimizing Kubernetes for new GPU hardware such as NVIDIA Vera Rubin is complex and error-prone. Drivers, kernels, runtimes, networking, operators, and schedulers must work together, with settings tuned to the hardware and workload. Small mismatches can prevent workloads from running or limit performance. The NVIDIA AI Cluster Runtime (AICR) —part of the NVIDIA DSX AI Factory Platform —simplifies this work by capturing tuned configurations in reusable recipes, generating bundles for existing deployment tools, and validating the running cluster. It will help Kubernetes environments be ready for NVIDIA Vera Rubin NVL72 compute trays on Day 1. Overall, the NVIDIA DSX AI Factory Platform brings together technologies to help the ecosystem design, build, and operate more efficient AI factories, including open source, modular software components purpose-built to support multi-tenant operations and scaling. Configuring new hardware architecture in a cloud-native stack requires plumbing updates from the lowest-level bindings all the way up to the orchestration layer. It is still early days for Vera Rubin. This post covers what changes if you run NVIDIA GB200 NVL72 today, what has been validated on early hardware access using NVIDIA GPU Operator and the Dynamic Resource Allocation (DRA) driver with Vera Rubin support. It also shows how AICR brings these components together in a configuration that platform teams can reproduce and verify.