The NVIDIA AI Cluster Runtime provides validated configuration packages to prepare Kubernetes environments for Vera Rubin NVL72 compute trays. The updated third-generation MGX rack-scale design integrates BlueField-4 DPUs and ConnectX-9 SuperNICs alongside a revised device topology. Because the tray exposes eight PCI device groups across two CPUs and four GPUs, administrators must adjust NUMA and GPU-to-NIC affinities. The deployment relies on Dynamic Resource Allocation for multi-node NVLink tasks and prioritizes whole-GPU allocation over Multi-Instance GPU configurations.
Key points
Bundling hardware-tuned configurations into reusable recipes allows platform teams to dep…: Bundling hardware-tuned configurations into reusable recipes allows platform teams to deploy complex cloud-native stacks immediately upon launch.
Operators must disable the host static IMEX service to prevent operational conflicts with…: Operators must disable the host static IMEX service to prevent operational conflicts with Dynamic Resource Allocation during multi-node NVLink tasks.
Current validated cluster runtime recipes strictly target whole-GPU allocation rather tha…: Current validated cluster runtime recipes strictly target whole-GPU allocation rather than supporting the architecture's new Multi-Instance GPU profiles.
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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…
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