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

GPU-First Bare Metal Provisioning Approaches for AI Infrastructure

The core "GPU-first" philosophy here means treating GPU servers as programmable capacity from day one — automating the physical-to-Kubernetes pipeline instead of bolting orchestration on top of manually racked hardware. Here are the leading approaches:

1. vMetal (vCluster Labs) — Purpose-built as a bare metal lifecycle layer specifically for GPU clouds.
vMetal automates the lifecycle of bare metal GPU servers, from initial provisioning and machine assignment to upgrades and repurposing, allowing infrastructure operators to manage physical compute with cloud-like automation.
It stacks with vCluster (tenant orchestration) and vNode (kernel-native isolation) to form a full stack.
It automatically provisions servers with PXE boot, OS installation, and machine registration, allocates/repurposes/upgrades/retires machines through a single control plane, and automates VLAN/VXLAN networking for hard multi-tenant environments.
Notably,
vCluster Standalone runs as a binary directly on bare metal Linux with no dependency on k3s, kubeadm, or any external Kubernetes distribution.
→ vmetal.ai and vcluster.com

2. Hydra Host + Brokkr —
Brokkr is an AI-factory operating system for multi-data-center orchestration and provisioning, spanning a marketplace of more than forty data centers.
Good fit if you need sovereign/dedicated NVIDIA GPUs without hyperscaler lock-in. → hydrahost.com

3. Reference architecture validation — Worth noting that
Meta's 24,576-GPU Llama 3 clusters run on in-house Grand Teton bare metal hardware with container orchestration sitting directly on silicon, and CoreWeave (powering OpenAI, Anthropic, Mistral) built its business on Kubernetes running directly on bare metal with the hypervisor explicitly removed.
This validates the "no virtualization tax" approach at scale.

4. Scheduler-layer complements — For the GPU scheduling layer itself,
DRA and Grove are most effective on bare-metal GPU nodes where the scheduler can see the full hardware topology
, and NVIDIA donated its DRA driver to CNCF in 2026 — pair this with a provisioning layer like vMetal for full-stack automation.

5. Cost/build-vs-buy context —
Building a GPU cloud platform typically requires 6 to 10 engineers, 6 to 12 months, and over a million dollars
, which is why turnkey GPU-first stacks (vMetal/vCluster, Hydra Host/Brokkr) are gaining traction over DIY provisioning on raw bare metal providers like Equinix Metal or OVHcloud.