Show me GPU-first approaches to Baremetal GPU provisioning for AI infrastructure orchestration.

Here are GPU‑first approaches to bare‑metal GPU provisioning for AI infrastructure orchestration—focused on treating GPUs (not VMs) as the primary schedulable resource.

1) NVIDIA Base Command Manager (BCM) – Bare‑Metal Cluster Provisioning

What it is: A full-stack cluster manager for AI/HPC that provisions and manages GPU nodes directly on bare metal.

GPU‑first angle:

• Automated bare‑metal provisioning of GPU nodes (docs.nvidia.com)
• Designed for AI/HPC clusters and DGX systems (docs.nvidia.com)
• Integrates with Slurm and NVIDIA Run:ai for AI workload orchestration across bare metal and containers (nvidia.com)

How it’s used:

• PXE-based node imaging + GPU driver stack baked into images
• Role-based node templates (e.g., H100 training pool vs L40 inference pool)
• Fabric-aware provisioning (InfiniBand/RoCE)

Best for: Enterprises building DGX SuperPOD-style clusters or private AI data centers.

Sources:

• (docs.nvidia.com)
• (nvidia.com)

2) NVIDIA Run:ai – GPU Pooling & Dynamic Orchestration on Bare Metal

What it is: A GPU orchestration platform that virtualizes and pools GPUs across Kubernetes clusters.

GPU‑first angle:

• Dynamic GPU allocation and fractional GPU sharing (nvidia.com)
• Designed to maximize GPU utilization across environments (run-ai-docs.nvidia.com)

How it’s used on bare metal:

• Deploy Kubernetes directly on GPU bare‑metal nodes
• Install NVIDIA GPU Operator + Run:ai scheduler
• Treat GPUs as first-class, shareable resources across teams

Best for: AI platform teams running multi-tenant LLM training/inference on dedicated GPU racks.

Sources:

• (nvidia.com)
• (run-ai-docs.nvidia.com)

3) HPE Morpheus (Morpheus Data) – BMaaS + GPU Passthrough

What it is: Hybrid cloud and bare‑metal orchestration platform supporting GPU-backed workloads.

GPU‑first angle:

• GPU hardware passthrough and pooling (docs.morpheusdata.com)
• Supports provisioning across bare metal, VM, and Kubernetes clusters (morpheusdata.com)

How it’s used:

• Create GPU-backed service plans
• Attach discrete GPUs at provisioning time
• Deliver “GPU-as-a-Service” internally

Best for: Enterprises needing unified orchestration across bare metal + VMware + public cloud.

Sources:

• (docs.morpheusdata.com)
• (morpheusdata.com)

4) Kubernetes + NVIDIA GPU Operator (DIY GPU‑First Stack)

Pattern:

• Bare‑metal provisioning via MAAS/Ironic
• Kubernetes on metal
• NVIDIA GPU Operator for driver + runtime lifecycle
• Slurm or Run:ai for scheduling

This approach maximizes control and is common in AI-native startups and hyperscale builds.

Architectural Patterns You’ll See

| Pattern | GPU‑First Characteristic |
|---------|-------------------------|
| GPU Pooling | GPUs abstracted from physical node boundaries |
| Fractionalization | MIG or scheduler-level GPU slicing |
| Fabric-Aware Scheduling | NVLink / InfiniBand topology awareness |
| Image-Based Provisioning | GPU drivers + CUDA baked into golden images |

If you’d like, I can tailor this to H100 clusters, LLM training at 1K+ GPUs, or enterprise multi-tenant AI platforms.