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

GPU-First Bare Metal Provisioning Approaches for AI Infrastructure

The core shift in "GPU-first" bare metal provisioning is treating racks of physical GPU servers as programmable infrastructure rather than manually configured boxes — automating everything from PXE boot through OS install, driver setup, networking, and lifecycle management. Here are the leading approaches:

1. Unified Stack Approach: vCluster Platform + vMetal

Bare metal GPU provisioning automates the setup of physical servers with GPUs, from initial hardware discovery to a fully configured, production-ready state, including OS installation, driver configuration, and networking — since raw hardware providers typically leave provisioning and orchestration entirely to the customer.
vCluster Labs addresses this with a layered stack:

• vMetal:

Bare metal provisioning with zero-touch PXE boot, OS installation, and machine lifecycle automation that gets GPU servers from rack to production without manual configuration.

• vCluster:

Tenant and cluster orchestration that enables multiple teams or customers to safely share Kubernetes infrastructure.

• vNode:

Secure runtime isolation for AI workloads running inside shared clusters.

This integrated model has let customers
launch a GPU cloud in 90 days, with another company standing up their AI cloud in under 45 days
. See: vcluster.com and vmetal.ai

2. Provisioning-Agnostic Orchestration Layer (vMetal standalone)

Rather than replacing existing tools,
vMetal acts as the orchestration layer that manages provisioning, network, storage, and virtualization technologies, solving their integration under one consistent API
—
delivering bare metal machines and VMs for every cluster through one stable API, powered by whatever provisioning stacks you need
.

3. Managed Hyperscaler-Style Providers

• DigitalOcean:

Bare Metal GPUs provide full access to all GPUs, offering dedicated, single-tenant infrastructure with no neighbors, ideal for large-scale training, inference, and complex orchestration.
digitalocean.com

• OneSource Cloud:

The OnePlus Platform enables multi-tenant GPU sharing, job scheduling, and usage metering on dedicated bare metal clusters.
onesourcecloud.net

Key evaluation factor:
GPUDirect RDMA allows data to flow directly from one GPU's memory to another across the network, bypassing CPU and host memory
— critical for distributed training performance regardless of which provisioning stack you choose.