GPU Rack Assembly: What It Is, What It Costs, and Who Does It
TL;DR
GPU rack assembly covers mechanical build, power cabling, NVLink routing, fiber testing, and commissioning. Learn what the process involves and how to evaluate providers for NVIDIA, Supermicro, and Dell GPU platforms.
GPU rack assembly is the physical process of building production-ready GPU infrastructure inside a data center. It covers everything between hardware delivery and the moment your operations team can begin loading AI training or inference workloads.
This is not the same as racking a traditional enterprise server. A standard 1U server has a few power cables and a couple of Ethernet connections. A modern GPU rack — particularly NVIDIA GB200 NVL72 or GB300 NVL72 — contains dozens of servers, hundreds of cable connections across multiple network fabrics, liquid cooling manifolds, and GPU-to-GPU interconnects that must follow platform-specific topologies precisely.
This guide covers what GPU rack assembly actually involves, what drives the cost, and how to evaluate companies that provide this service.
What GPU Rack Assembly Includes
The term "rack assembly" undersells the scope. A complete GPU rack deployment includes five distinct phases of work, each requiring different skills and tools.
Mechanical assembly
This is what most people picture: servers going into racks. Rail kits installed, servers slid into position, switches mounted, PDUs (Power Distribution Units) secured. For liquid-cooled platforms like NVIDIA GB200 NVL72 and GB300 NVL72, this phase also includes CDU (Coolant Distribution Unit) positioning and rack-level coolant manifold installation.
Mechanical assembly is the most straightforward phase, but it sets the foundation for everything that follows. Incorrect rail installation creates serviceability problems months later when a server tray needs to be pulled for maintenance. Incorrect PDU positioning creates cable management nightmares during the power phase.
Power cabling
Connecting PDUs to servers, verifying redundant power paths, routing and labeling cables. Power density is the key variable. An air-cooled NVIDIA H100 HGX rack draws approximately 10-12kW. A liquid-cooled GB200 NVL72 rack draws approximately 120kW. A GB300 NVL72 rack draws even more.
At 120kW per rack, power cabling is no longer a simple plug-and-play exercise. Multiple high-amperage feeds per rack, proper cable routing to maintain airflow (or avoid interfering with coolant lines), and labeling that enables rapid fault isolation during production.
Network cabling
This is where GPU infrastructure diverges completely from traditional data center work, and where most deployment errors occur.
A single GPU rack in an AI training cluster connects to multiple network fabrics simultaneously:
Management network. Standard Ethernet (1GbE or 10GbE) for IPMI, BMC, and out-of-band management. Typically connects to Arista, Cisco, or similar switches.
High-speed data fabric. InfiniBand (NVIDIA Quantum switches, NDR or XDR speeds) or high-speed Ethernet (400GbE or 800GbE, often Arista 7800 or NVIDIA Spectrum switches). This is the inter-rack GPU communication fabric used during distributed training.
NVLink interconnects. Direct GPU-to-GPU connections within an NVLink domain. On NVIDIA H100 HGX, NVLink connects 8 GPUs within a single server tray. On GB200 NVL72 and GB300 NVL72, NVLink connects 72 GPUs across an entire rack through NVSwitch trays — a far more complex topology requiring precise cable routing.
Storage network. Connections to distributed storage (often NVMe-oF over high-speed Ethernet) for dataset access during training.
Cable types vary by connection: OM4 and OM5 multimode fiber for short intra-row runs, OS2 single-mode fiber for longer distances, MPO/MTP trunk cables for high-density fiber aggregation, DAC (Direct Attach Copper) for short switch-to-server connections, AOC (Active Optical Cables) for medium distances, and AEC (Active Electrical Cables) for emerging high-speed copper interconnects.
The NVLink cabling is the most technically demanding step. Each NVIDIA platform generation has a different NVLink topology. The routing is not interchangeable between generations. A technician who has cabled H100 NVLink cannot assume the same routing works for GB200 NVL72. The topology is completely different, and a single misrouted cable degrades the entire NVLink domain.
Testing
Every individual connection — fiber and copper — must be tested and documented before the system moves to commissioning. This includes:
- OTDR (Optical Time Domain Reflectometer) testing on every fiber connection, producing a trace that maps the full link with distance and loss measurements at every event (connectors, splices, stress points)
- Insertion loss and return loss measurements on every fiber connection
- Copper certification to the applicable TIA category standard
- Per-connection test results documented and delivered as part of the project handoff
Testing is not optional and not something to rush through at the end. A fiber connection that passes a simple continuity test can still have a marginal connector that fails under sustained high-bandwidth AI training traffic. OTDR testing catches these problems. Basic pass/fail testing does not.
Commissioning
System-level validation that the entire rack (or cluster) works together:
- POST (Power-On Self-Test) on every server — confirming all GPUs are detected, NVLink links are active, firmware versions are correct
- Network fabric validation — all switch-to-server connections operational
- Liquid cooling validation — confirming thermal performance under load (for liquid-cooled platforms)
- Documentation package delivery — cable maps, test results, OTDR traces, rack elevation drawings, photographs
A properly commissioned rack is production-ready. Your operations team should be able to begin loading workloads immediately after handoff, with no rework and no punch list.
What Drives the Cost
GPU rack assembly cost varies significantly based on four factors:
Platform complexity. Air-cooled H100 HGX is the fastest and simplest to deploy. Liquid-cooled GB200 NVL72 takes significantly longer due to cooling integration, higher cable density, and NVLink topology complexity. GB300 NVL72 is even more complex. Labor hours per rack scale roughly with platform complexity.
Scale. Deploying 20 racks and deploying 500 racks are different projects. Larger deployments can be more efficient per rack (crew ramp-up cost is amortized), but they also require more coordination, more simultaneous crew capacity, and more project management.
Testing requirements. Full OTDR testing on every fiber connection takes time and requires expensive equipment. Some buyers accept basic insertion loss testing only. Others require full OTDR traces on every link. The testing scope directly affects labor hours per rack.
Facility readiness. If the deployment partner shows up and the facility power, cooling, and raised floor aren't ready, the crew sits idle or works around obstacles. This adds cost. The most efficient deployments happen when facility infrastructure is 100% complete before GPU hardware arrives.
Most GPU deployment partners price on a per-rack or per-project basis, scoped during the discovery phase after reviewing the hardware BOM, platform type, and facility conditions. Expect pricing discussions to center on crew size, duration, platform complexity, and testing scope — not on a simple hourly rate.
Who Provides GPU Rack Assembly Services
The market has three categories of providers:
Hardware OEMs. Dell, Supermicro, and NVIDIA offer deployment services bundled with hardware purchases. Dell's ProDeploy program, for example, can include rack integration for Dell PowerEdge platforms. The advantage is a single vendor for hardware and installation. The limitation is that OEM deployment services typically only cover their own hardware, and may not have the specialized GPU platform expertise needed for complex NVLink routing or multi-vendor deployments.
Staffing-model companies. These companies maintain large technician networks (500-1,000+ technicians) and dispatch crews on a per-project basis. They provide scale and geographic coverage. The limitation is variable quality — your project gets whichever technicians are available in that window, and platform-specific expertise depends on luck.
Operator-led deployment companies. These companies maintain smaller, dedicated crews (30-100 technicians) trained specifically on GPU infrastructure. They deploy with their own leadership on-site, embed QC in the process, and specialize in NVIDIA platform deployments. The limitation is smaller geographic footprint and fewer simultaneous projects, but for concentrated deployments at one or two facilities, they typically deliver faster and cleaner.
Planning a GPU Rack Assembly Project?
Leviathan Systems provides GPU rack assembly services across the United States, covering NVIDIA H100, H200, GB200 NVL72, and GB300 NVL72 platforms on Supermicro, Dell, and NVIDIA hardware with Arista and NVIDIA switching infrastructure. Our teams have assembled over 1,500 GPU racks and deployed more than 25,000 cable connections at hyperscale AI training facilities. We are operator-led — our founders are on-site during deployments — and we mobilize within one week of contract execution.