A GPU deployment is the process of physically installing GPU hardware into a data centre and getting it to a point where it works powered up, cooled, connected to the network and ready to run workloads. For organisations that are undertaking artificial intelligence (AI) infrastructure deployments, especially to support generative AI this stage matters more than many expect.
A standard server installation is relatively forgiving. The hardware is light making the power draw modest. Meanwhile the cabling is straightforward, and the cooling requirements are well within what most data centres handle daily. Although you wouldn’t want to – you can make a few small mistakes and it still works fine.
GPU deployments on the other hand, especially at the scale that modern AI systems, deep learning, and large-scale AI are a different discipline entirely.
The hardware is heavy, the compute power requirements are enormous. Furthermore, the cooling requirements are so intense that air cooling often can’t keep up and the cabling is intricate. This makes the tolerance for getting things wrong essentially zero, because every mistake either shows up as a failed component or as degraded performance that costs real money over time.
We’ve been deploying complex data centre infrastructure for 25 years and enterprise ai deployment services are some of the most technically demanding projects we do. In the blog we’re going to walk you through what’s actually involved…
The Hardware Itself
When people talk about GPU data centre deployments for AI, they’re usually talking about NVIDIA GPU installations. NVIDIA’s H-series, B-series, and GB-series GPUs are the prominent choice for enterprise AI training and inference, and each generation is more powerful and more demanding to deploy than the last!
The NVL72 is a good example. It is a single-rack setup that holds 72 H100 or H200 GPUs. They are all linked through NVLink, with very high bandwidth. It’s one of the most powerful single-rack compute configurations available and also one of the most complex things you can install in a data centre.
The rack doesn’t arrive assembled and ready to plug in. It gets built onsite by specialist engineers who know exactly how the components fit together, how the NVLink cables route, how the cold plates connect to the cooling loop, and how the power distribution needs to be configured. Get any of that wrong and you either can’t power it on, or you power it on and it fails to deliver the expected performance for machine learning models and training AI workloads.
Here at Technimove we’ve built NVL72 racks onsite under tight enterprise deadlines. This level of GPU deployment requires engineers who’ve done it before.
The Power Requirements
A single NVL72 rack can draw over 120kW. To put that in context, a typical server rack in a conventional enterprise data centre draws somewhere between 5 and 15kW. So you’re looking at power density that’s ten times higher sometimes more.
That means the data centre facility needs to be assessed carefully before any hardware arrives. Can the floor handle the load? Is there a power feed available that can support the draw? Is the distribution infrastructure between the mains and the rack specified correctly? These aren’t things you want to discover on installation day.
Technimove work through these questions during the site assessment phase before equipment gets ordered or delivery dates are confirmed. Although not the most exciting part of a GPU deployment, it’s the part that prevents expensive surprises!
The Cooling Challenge
Heat is a constant challenge in a high-density GPU deployment. The 72 GPUs in an NVL72 are generating heat at a rate that air cooling can’t reliably manage. Liquid cooling is the answer.
Use direct-to-chip systems where coolant flows through a loop.
The loop connects to a cold plate on top of each GPU.
The cooling system has to be installed in parallel with the rack build.
Stage 1: The manifolds go in.
Stage 2: The cold plate connections are made
Stage 3: The loop is pressure tested before any coolant goes in
Stage 4: The flow rates are verified before power-on.
None of these stages are optional. You do not power on a 120kW rack without working liquid cooling. This is one of the reasons GPU deployments need specialist engineers rather than generalist data centre teams. The liquid cooling work is a discipline in its own right.
The Cabling
High‑performance AI systems depend on equally high‑performance networks.
GPU clusters rely on InfiniBand at 200Gb/s or 400Gb/s, fibre links between racks, active optical cables, direct attach copper, and dense MPO fibre assemblies. These networks connect GPUs to switches, storage platforms, and data lakes, ensuring workloads have the throughput required for training and inference.
Every connection must be planned, installed and certified. At these speeds, even slightly out-of-spec cabling won’t just slow down. It can cause intermittent failures that disrupt real-world AI applications.
This is why we certify every cable run as part of its AI infrastructure deployment projects.
Security and Compliance
Running AI systems at scale also introduces security and compliance requirements.
Many platforms handle sensitive data or intellectual property, making physical security and logical access controls just as important as performance. These considerations must be addressed across on‑prem environments and hybrid models that integrate local infrastructure with cloud services.
Why It Matters Who Does This
The reality is that GPUs are only as effective as the infrastructure around them. Poor power design, inadequate cooling, weak cabling or missing security considerations can undermine even the best hardware.
Technimove delivers end‑to‑end enterprise AI deployment services, covering site assessment, NVIDIA GPU installation, rack build, cooling, cabling, power, commissioning and handover. We remain involved after go‑live to ensure platforms continue to support evolving workloads over the long term.
If you’re planning a GPU or AI infrastructure deployment, and you want it done right, talk to our experts today.If you’re planning a GPU or AI infrastructure deployment, and you want it done right, talk to our experts today. If you’re planning a GPU or AI infrastructure deployment, and you want it done right, talk to our experts today.