Bare-metal GPU infrastructure

Dedicated NVIDIA GPUs without cloud roulette.

GlassPearl deploys whole NVIDIA H100, H200, B200, and B300 servers for AI teams that need steady capacity, full root, and predictable economics. One server, one clear price, one tenant: you.

Get a quote Compare nodes
8×
NVIDIA GPUs per node
80kW
Cabinets for dense clusters
1
Tenant per server
  • Full rootNo hypervisor, shared lanes, or instance packing.
  • Cluster fabric400 Gb/s InfiniBand or 800 Gb/s NVIDIA SN5610 Spectrum-X.
  • Flat contractWhole servers handed over without per-instance metering.
Short version

Virtual GPUs are for trying. Bare metal is for shipping.

Virtual GPU instances are useful when a team is still exploring, testing notebooks, or proving demand. That is not what GlassPearl sells. GlassPearl sells the point where GPU work stops being casual and starts being infrastructure.

A bare-metal GPU server gives one team the whole physical machine: GPUs, CPU, memory, storage, NICs, root access, and the fabric plan. No hypervisor tax, no noisy-neighbor story, no wondering which part of the shared platform is slowing the run down.

GPU economics

Save money on GPUs that run all day.

AWS, Azure, and Google Cloud are priced for flexibility. GlassPearl is priced for production GPU fleets where the machines stay busy.

Bring the published cloud quote and we'll price the same workload on whole H100, H200, B200, or B300 servers — pricing is quote-based, not posted.

Cloud cost savings More than 60% lower

In published H100 and B200 cloud comparisons. B300 also comes in far below published AWS pricing. Actual savings depend on GPU class, region, topology, and provider quote.

H100

Cut H100 spend when the GPUs run daily.

If H100s are part of the production stack, stop pricing them like short-lived cloud experiments. Quote the whole server and keep the savings.

B200

Make Blackwell economics work.

B200 demand carries a cloud premium. GlassPearl keeps the hardware whole and prices it for teams that know the GPUs will stay busy.

B300

Get B300 without cloud markup pain.

B300 is available as full servers for serious AI work. The value is the machine, the control, and the lower long-run cost curve.

Enterprise security

Security teams like infrastructure they can draw on a whiteboard.

Sensitive AI work is not just a compute purchase. It is a security review, a data-governance conversation, and often a procurement checkpoint. Bare metal gives those teams a simpler object to approve: a physical GPU server with a clear owner, a known control plane, and root-level authority over the runtime.

It does not replace encryption, IAM, patching, monitoring, or incident response. It gives the enterprise a cleaner place to enforce those controls.

Clear asset boundary

A physical GPU server can be assigned to one company, one environment, and one operating model. That is easier for security, legal, and procurement teams to reason about.

Root-controlled baseline

Your team controls the OS image, driver stack, hardening policy, endpoint agents, logging, and patch cadence so internal security standards can be applied directly.

Network policy you can draw

Private connectivity, firewall rules, jump hosts, key management, monitoring, and admin paths can be designed around a known server boundary.

Data and model custody

Training data, checkpoints, and model weights live on infrastructure with a clear owner and a clear lifecycle, from burn-in to handoff to reimage.

The model

Compute you own the whole of.

Most GPU clouds rent you a slice and make you manage availability, metering, and performance variance. GlassPearl does the opposite: the whole server, a fixed contract, and a direct handoff to your team.

01

Whole-server handoff

You get the entire machine: bare metal, full root, no hypervisor tax, and no neighbors sharing PCIe lanes. What the hardware can do, you can do.

02

Flat whole-server pricing

A fixed price for the full server. No per-instance metering, no utilization surprises, no scramble when spot capacity evaporates mid-run.

03

Dedicated to real work

Training runs, inference fleets, and research environments get a stable home. One server handoff; no re-bidding for GPUs every quarter.

04

Deployment handled

We source, rack, cable, burn in, and hand over the server in power-dense colocation with 400 Gb/s InfiniBand or 800 Gb/s NVIDIA SN5610 Spectrum-X where the cluster calls for it.

Hardware

NVIDIA GPUs, whole and dedicated.

H100, H200, B200, and B300 servers are handed over whole, with scale-out fabric options for clusters that need to act as one. Single-node work stays simple on internal NVLink; multi-node work gets the fabric it needs.

Available now

H100 Node

8 GPU
GPUs
8× H100
Memory
80 GB HBM3 / GPU
Interconnect
NVLink + scale-out fabric
Handoff
Single-tenant bare metal
Available now

H200 Node

8 GPU
GPUs
8× H200
Memory
141 GB HBM3e / GPU
Interconnect
NVLink + scale-out fabric
Handoff
Single-tenant bare metal
Available now

B200 Node

8 GPU
GPUs
8× HGX B200
Memory
180 GB HBM3e / GPU
Interconnect
NVLink + ConnectX-8
Power
~14.5 kW / node
Chassis
SYS-A22GA-NBRT
Available now

B300 Node

8 GPU
GPUs
8× B300 (Blackwell Ultra)
Density
4 nodes / 80 kW cabinet
Power
~19 kW peak / ~14.5 kW sustained
Chassis
SYS-822GS-NB3RT

Scale-out fabric

Single nodes run on internal NVLink. Multi-node clusters are stitched together with 400 Gb/s InfiniBand or 800 Gb/s NVIDIA SN5610 Spectrum-X, depending on topology, so large training jobs scale across racks without a network bottleneck or unnecessary single-node fabric spend.

InfiniBand
400 Gb/s
Spectrum-X
800 Gb/s SN5610
How it works

From spec to root access.

  1. 01

    Scope the workload

    Share the GPU count, node preference, topology, target date, and what the cluster will run. We turn that into a server and fabric plan.

  2. 02

    Lock the quote

    Choose the dedicated servers and pricing model that fit the workload. The full server is yours instead of sold as metered slices.

  3. 03

    We rack & burn-in

    Nodes are deployed, cabled, and stress-tested in power-dense colocation. Multi-node InfiniBand or Spectrum-X fabric is validated before handoff.

  4. 04

    Root handed over

    You receive bare-metal access to the whole server. Bring your OS, CUDA stack, scheduler, and monitoring; root is yours top to bottom.

Colocation

Own the hardware already?

If you'd rather bring your own GPUs, GlassPearl brokers power-dense colocation — sourcing the cabinets, power, and scale-out fabric-ready space your cluster needs, and matching you to the right facility.

Talk colocation
  • Power-dense cabinetsUp to 80 kW per cabinet for sustained GPU draw.
  • Fabric-readySpace provisioned for InfiniBand or Spectrum-X clustering.
  • Right facility, right fitWe match capacity to your deployment timeline.
Request capacity

Get a capacity quote.

Send the GPU count, node preference, topology, and target start date. We'll come back with hardware, facility assumptions, and pricing.

Email capacity request Review hardware