// DATA CENTRE / AI-READY INFRASTRUCTURE
The data hall was never built for the GPUs going into it.
AI compute draws power and throws off heat the existing facility cannot handle. We assess what the hall can take, design the power, cooling, space and fabric to host it, build it, then commission and hand it over under your brand.
Scope an AI-ready buildThe GPUs are ordered. The facility cannot host them.
A legacy rack was provisioned for roughly 8kW. A single AI rack now pulls 132kW or more, weighs far past standard floor loading, and rejects heat that air cooling cannot move past about 30kW. Most data halls cannot support that density today, so the bottleneck on AI is rarely the compute order; it is the power feed, the cooling loop, the floor strength and the back-end fabric. We close that gap: assess the hall, engineer the high-density power and liquid cooling, build the GPU racks and InfiniBand or Spectrum-X fabric, then commission and hand it over so your client's AI estate has somewhere to live.
What we deploy.
AI Readiness Assessment
A facility audit before a single GPU lands: power headroom per rack, cooling capacity, floor loading and cabinet space, plus the structured-cabling and fabric paths. We tell you what the existing hall can take and exactly where it falls short.
High-Density Power
Overhead busways, high-density PDUs and the redundancy to match, engineered for racks that now pull 132kW and more. We size the feeds, the distribution and the failover so a full DGX rack draws cleanly instead of tripping the floor it sits on.
Liquid Cooling Design
Direct-to-chip cold plates and rear-door heat exchangers fed by coolant distribution units, designed for the thermal load GB200 and HGX racks throw off. Air alone stops working past roughly 30kW, so we engineer the liquid loop, the CDUs and the facility-water tie-in.
GPU Compute Build
NVIDIA DGX and HGX racks built, cabled and powered: the physical install, the structured cabling, the power drops and the airflow or liquid path. The compute lands racked, labelled and ready for the AI platform team to provision.
AI Fabric
The low-latency back-end network that keeps GPUs fed: NVIDIA Quantum-X InfiniBand or Spectrum-X Ethernet, cabled and validated for the non-blocking bandwidth large training and inference jobs depend on. A GPU starved by the fabric is an expensive idle GPU.
Commissioning & Handover
Burn-in under synthetic GPU load, thermal validation across the loop, power-draw verification and a documented runbook. We prove the hall holds the load, then hand it over with the as-built drawings and operating notes your team runs from.
The numbers behind the density problem.
How the options compare.
What your client gets from a traditional data centre build, a generic integrator, or our team under your brand.
AI-ready infrastructure, answered straight.
01 Can our existing data hall take GPUs?
Usually not without work, and the assessment tells you how much. A legacy rack was provisioned for roughly 8kW; a single AI rack now pulls 132kW or more, weighs far past standard floor loading, and rejects heat air cannot move. We audit power headroom, cooling capacity and floor strength first, then tell you whether the hall extends or whether a dedicated zone or colocation is the saner path.
02 Do we actually need liquid cooling?
Past roughly 30kW per rack, air cooling runs out of room, and dense GB200 and HGX racks sit well above that. Direct-to-chip cold plates pull heat straight off the GPUs and rear-door exchangers catch the rest, fed by coolant distribution units tied into facility water. We design the loop to the specific rack density rather than retrofitting a workaround later, because adding liquid after the fact means tearing the hall up twice.
03 Colocation or on-prem for GPU compute?
It depends on the power and cooling your site can realistically deliver. Purpose-built colocation already has the high-density power, liquid-ready cooling and floor loading that most enterprise halls were never designed for, so it is often faster and cheaper to land compute there. On-prem makes sense where data gravity, latency or compliance demand it. We model both against your facility, your timeline and your data, then build whichever path you choose.
04 How does this differ from your AI Infrastructure offering?
This page is the facility: the power, cooling, racks and fabric that physically host AI compute, commissioned and handed over. The AI Infrastructure offering under AI Services is the compute and software stack that runs on top, NVIDIA DGX sizing, Run.ai scheduling, NeMo and Triton for training and serving. One builds the room and the iron; the other builds the platform inside it. They are designed to dovetail, so a partner can take a client through both without overlap.
05 Who operates the data centre after handover?
You or the client does. Belico designs, builds, commissions and hands over the facility with as-built drawings, thermal validation results and an operating runbook. We are not the ongoing facilities team and we do not run the hall. The runbook and burn-in evidence are written so your operations staff, or the client's, can take the load from day one with confidence.
Tell us what your clients need.
A tri-cloud migration. A 200-site SD-WAN rollout. A security architecture before NIS2 hits. An AI system your client is asking about next quarter. We scope it, staff it, and deliver it under your brand. One conversation tells us if we are the right team.