How to Hire a GPU Infrastructure Deployment Team

TL;DR

A practical guide for data center operators evaluating GPU infrastructure deployment partners. Covers NVIDIA, Supermicro, Dell, Arista, and AMD platforms. Learn what to ask, how to scope, and what to expect.

You have GPU hardware on order — or already arriving. NVIDIA DGX or HGX systems, Supermicro GPU servers, Dell PowerEdge platforms, Arista switching infrastructure. Hundreds of racks. Tens of thousands of cable connections. A facility that needs to go live on a deadline.

The hardware vendors ship hardware. They do not send a crew to your data center to assemble, cable, test, and commission it. That is on you.

This guide covers how to evaluate and hire a GPU infrastructure deployment partner — the company that puts boots on the ground at your facility and turns pallets of equipment into production-ready AI infrastructure.

Who Needs a Deployment Partner

Not every GPU deployment requires an outside team. A small proof-of-concept cluster — ten or twenty racks of air-cooled NVIDIA H100 HGX servers with standard Ethernet networking — can often be handled by an experienced internal team.

But most large-scale GPU deployments have moved past that threshold. The current generation of AI infrastructure — NVIDIA GB200 NVL72, GB300 NVL72, and upcoming Vera Rubin platforms — requires liquid cooling integration, complex NVLink topologies, high-density fiber cabling, and rack power densities exceeding 120kW. These deployments need specialized labor that most data center operations teams do not have in-house.

You need a deployment partner when:

Your deployment involves 50 or more GPU racks
The platform requires liquid cooling (all GB200 and GB300 deployments)
NVLink interconnects must be routed across a multi-GPU domain (NVL72 and larger)
Your timeline requires parallel crew operations across multiple rows or halls
Your internal team has not deployed the specific NVIDIA platform generation being installed
You are deploying mixed-vendor infrastructure (for example, Supermicro servers with Arista switches and NVIDIA GPUs)

Two Deployment Models: Staffing Networks vs. Operator-Led Companies

The GPU infrastructure deployment market has two fundamentally different service models. Understanding the difference is critical to selecting the right partner.

Staffing network model

Staffing-model companies maintain a large database of independent technicians — often 500 to 1,000 or more across multiple countries. When a project comes in, they recruit technicians from this database and dispatch them to the client's site. The company provides project management and logistics. The technicians may be employees or subcontractors.

This model works for high-volume, geographically distributed deployments where the work is relatively standardized — legacy server refresh, basic rack-and-stack, structured cabling in traditional enterprise data centers. The staffing model provides scale and geographic coverage.

The limitation is quality consistency. Technicians rotate between projects and clients. They may have deployed NVIDIA H100 last month and a completely different vendor this month. The client's project manager typically owns quality control on-site. Platform-specific expertise depends on whichever technicians happen to be available for your project window.

Operator-led model

Operator-led companies maintain a smaller, dedicated crew — typically 30 to 100 technicians — trained specifically on GPU infrastructure platforms. The company's leadership is directly involved in project execution. Quality control is embedded in the deployment process rather than delegated to the client.

This model works for complex, platform-specific GPU deployments where errors are expensive. When every NVLink connection must be routed to a specific topology, when liquid cooling manifolds must be pressure-tested before coolant fill, when POST verification must confirm all 72 GPUs in an NVL72 rack — the operator model provides tighter execution.

The limitation is scale and geography. An operator-led company with 50 technicians cannot staff 10 simultaneous deployments across 10 countries. But for a concentrated deployment at one or two facilities, the operator model typically delivers faster completion, fewer rework cycles, and a cleaner handoff.

What a Deployment Partner Actually Does

A GPU infrastructure deployment partner handles the physical work between hardware delivery and production readiness. Here is the scope, broken into five phases.

Phase 1: Mechanical assembly

Rail installation into racks. Server placement. Switch placement. PDU mounting. Cable management hardware installation. For liquid-cooled platforms (GB200 NVL72, GB300 NVL72), this phase also includes CDU (Coolant Distribution Unit) positioning and rack-level manifold routing.

This is the phase most people think of as "rack and stack." On current-generation NVIDIA platforms, it is the simplest phase. The complexity comes next.

Phase 2: Power cabling

PDU-to-server power connections. Redundant power path verification. Cable routing and labeling. On high-density GPU racks, power cable management is significant — a GB300 NVL72 rack draws over 120kW, requiring multiple high-amperage power feeds per rack.

Phase 3: Network cabling

This is where GPU deployments diverge completely from traditional data center work.

A standard enterprise server has a few Ethernet connections. A GPU rack in an AI training cluster has dozens of connections per rack across multiple cable types and network fabrics:

Management network: Standard Ethernet for IPMI/BMC, typically 1GbE or 10GbE
High-speed data fabric: InfiniBand (NDR/XDR) or high-speed Ethernet (400GbE/800GbE) for GPU-to-GPU communication across racks, often using Arista, NVIDIA Spectrum, or NVIDIA Quantum switches
NVLink interconnects: GPU-to-GPU connections within a rack or NVLink domain — these follow platform-specific topologies that change with every NVIDIA generation
Storage network: Connections to shared storage or NVMe-oF fabric, typically high-speed Ethernet

Cable types include OM4 and OM5 multimode fiber, OS2 single-mode fiber, MPO/MTP trunk cables, DAC (Direct Attach Copper), AOC (Active Optical Cables), and AEC (Active Electrical Cables). The correct cable type depends on distance, bandwidth requirement, and switch port type.

NVLink routing is the most error-prone step in GPU deployment. Each NVIDIA platform generation has a different NVLink topology. An H100 HGX baseboard routes NVLink across 8 GPUs within a single server tray. A GB200 NVL72 routes NVLink across 72 GPUs spanning an entire rack — a fundamentally different and far more complex cabling job. A single misrouted NVLink cable can degrade an entire GPU domain's training performance.

Phase 4: Testing

Every connection must be tested individually before the system can be commissioned. This includes:

OTDR (Optical Time Domain Reflectometer) testing on every fiber connection — this characterizes the full fiber link, identifying connectors, splices, stress points, and loss at each event
Insertion loss and return loss measurement on every fiber and copper connection
Copper certification to the appropriate TIA category standard
Per-connection documentation of test results

Deployment partners that skip OTDR testing and only perform basic continuity or pass/fail checks are cutting a critical corner. A marginal fiber connection that passes a simple power meter test can fail under sustained high-bandwidth GPU training workloads.

Phase 5: Commissioning and handoff

System-level validation that everything works together:

POST (Power-On Self-Test) verification on every server — all GPUs detected, NVLink active, firmware versions correct
Network fabric validation — all switch-to-server connections verified
Liquid cooling validation — thermal performance under load (for liquid-cooled platforms)
Documentation package delivery — cable maps, test results, OTDR traces, rack elevation drawings, photographs

Production-ready means your operations team can begin loading workloads on the same day the deployment partner hands off. No rework. No punch lists.

What to Ask a Deployment Partner

These questions separate experienced GPU infrastructure deployers from companies that treat GPU racks as generic data center work.

Platform experience

"Which NVIDIA platform generations has your team deployed?"

This is the single most important question. Deploying H100 air-cooled racks does not qualify a team for GB300 NVL72 liquid-cooled deployments. Each NVIDIA generation has different assembly procedures, NVLink topologies, power requirements, and cooling configurations. The jump from air-cooled H100 to liquid-cooled GB200 is not incremental — it is a fundamentally different build.

Ask for specifics: How many racks of each platform? At which facilities? What was the crew size and timeline?

Vendor experience

"Which hardware vendors has your team deployed?"

GPU infrastructure is multi-vendor. A single deployment might include Supermicro GPU servers, NVIDIA DGX or HGX systems, Arista or NVIDIA Quantum switches, and Dell infrastructure servers. Some deployments include AMD Instinct GPUs on Supermicro or Dell platforms. Your deployment partner should have direct experience with the specific vendor combination in your BOM.

Quality control

"Who manages quality control on-site, and what does the QC process look like?"

In the staffing model, QC often falls on the client's project manager. In the operator model, QC should be embedded in the deployment process with the company's leadership inspecting work at each phase.

Ask specifically: Is there a lead technician inspecting each rack before it moves to the next phase? Who signs off on commissioning? Are the company's principals on-site during deployment, or managing remotely?

Testing and documentation

"What testing do you perform on every connection, and what documentation do you deliver?"

The right answer includes OTDR testing on fiber, insertion loss and return loss measurements, copper certification, and a per-connection documentation package. If a deployment partner cannot describe their testing methodology in detail, that is a red flag.

Liquid cooling

"Do you handle liquid cooling integration in-house, or do you subcontract it?"

For GB200 and GB300 deployments, liquid cooling is not optional. CDU installation, rack-level manifold routing, quick-disconnect fittings, leak detection wiring, pressure testing, and thermal commissioning must be part of the deployment scope. If the deployment partner subcontracts the cooling work, you now have two companies to coordinate — and a gap in accountability between the server team and the cooling team.

Mobilization timeline

"How quickly can you mobilize a crew to my facility after contract execution?"

Industry standard is two to four weeks. Companies that rely on subcontractor sourcing may need longer. Companies with dedicated crews can mobilize in under one week.

References

"Can I speak with a reference from a similar deployment — same platform, similar scale?"

A deployment partner that has done the work should be able to provide at least two references from comparable projects. If they cannot, they may be stretching their experience.

Scoping Your Project

The deployment partner should scope your project during the discovery phase. But you can prepare by assembling this information before the first conversation:

Hardware BOM — exactly which servers, switches, and GPUs are being deployed, including vendor and model
Rack count — total number of GPU racks, networking racks, and storage racks
Platform type — H100, H200, GB200, GB300, or mixed
Cooling type — air-cooled, liquid-cooled, or both
Facility readiness — is power and cooling infrastructure complete, or still under construction?
Timeline — when does hardware arrive? When does the facility need to be production-ready?
Standards and compliance — any specific requirements (TIA-942, BICSI, internal standards)?
Site access — any restrictions on crew access, badge requirements, or work windows?

The single biggest variable in deployment timeline is platform complexity. An air-cooled H100 HGX rack can be assembled, cabled, and commissioned significantly faster than a liquid-cooled GB300 NVL72 rack with its higher cable density, NVLink topology complexity, and cooling integration requirements.

Red Flags

Watch for these when evaluating deployment partners:

No platform-specific experience. "We deploy all platforms" without being able to describe the differences between them.
Cannot describe their QC process. Quality control should be specific and documented, not "we check everything."
Subcontracting the technical work. If the company you are contracting with is hiring a different company to do the actual deployment, you are paying a margin for project management, not execution.
No documentation deliverables. A deployment partner that does not deliver per-connection test results, cable maps, and OTDR traces is not doing the job completely.
Unrealistic timelines. If someone quotes you two weeks for 200 liquid-cooled GB300 NVL72 racks, they do not understand the scope.
No references from comparable projects. Experience deploying H100 does not automatically translate to GB300. Ask for platform-specific references.

Planning a GPU Deployment?

Leviathan Systems deploys GPU infrastructure from NVIDIA H100 through GB300 NVL72 across the United States. We have assembled over 1,500 GPU racks and deployed more than 25,000 cable connections at hyperscale AI training facilities operated by companies including Meta, Oracle, and xAI. Our teams deploy infrastructure built on hardware from NVIDIA, Supermicro, Dell, and Arista. We are operator-led — our founders are on-site during deployments — and we mobilize crews within one week of contract execution.

If you are planning a GPU infrastructure deployment, our engineering team can scope your project within one business day.