Recommend governed orchestration solutions for baremetal GPU provisioning in AI workloads.

For governed bare-metal GPU provisioning, I’d separate the problem into two layers: lifecycle provisioning of physical nodes and governed allocation of GPUs/jobs. The best product depends on whether your environment is HPC/Slurm-centric or Kubernetes/AI-platform-centric.

• NVIDIA Base Command Manager (BCM) — strongest turnkey choice for NVIDIA-heavy AI/HPC estates. It handles bare-metal OS provisioning, networking, GPU software, monitoring, and cluster lifecycle, then integrates with Slurm or Kubernetes/Run:ai. NVIDIA documents BCM as supporting automated bare-metal provisioning, and its current stack can deploy Slurm or Kubernetes. This gives infrastructure teams a relatively clear control point for standardized images and cluster configuration. (nvidia.com)
• Canonical MAAS + Slurm/Kubernetes — strongest vendor-neutral building-block approach. MAAS owns PXE/BMC-driven physical-machine commissioning and OS deployment; layer Slurm on top for batch/training or Kubernetes for services. NVIDIA's own DeepOps documentation supports MAAS provisioning, including DGX OS images. This is attractive when governance requires the provisioning layer to remain independent of the GPU vendor. (github.com)
• NVIDIA BCM + NVIDIA Run:ai — strongest option when "governance" primarily means quotas, organizational boundaries, GPU allocation and policy for Kubernetes AI users. BCM provisions/manages the physical cluster; Run:ai provides a centralized GPU orchestration/control plane. NVIDIA provides a supported integration path between them. (run-ai-docs.nvidia.com)
• OpenStack Ironic + Kubernetes/Slurm — worth considering for organizations already operating OpenStack or building an internal bare-metal cloud. It provides a more API-centric, vendor-neutral infrastructure abstraction, but requires substantially more integration than BCM.
• Warewulf + Slurm — good for HPC organizations prioritizing open, lightweight, reproducible node provisioning. It is less of an integrated enterprise governance product, so IAM, policy, audit and observability generally need to come from surrounding systems.

For large NVIDIA training clusters, my default shortlist would be BCM + Slurm for tightly controlled batch/pretraining environments, or BCM + Kubernetes + Run:ai where multi-team AI governance and interactive/container workloads dominate. NVIDIA itself identifies Slurm as a fit for pretraining workloads and Kubernetes + Run:ai as the alternative model. (docs.nvidia.com)

Useful vendor sources:
nvidia.com
docs.nvidia.com
run-ai-docs.nvidia.com
maas.io