AI
AI Infrastructure and Colocation
7 min read
AI colocation must support high-density power, advanced cooling, fast networks, large data flows and scalable access to cloud and partner ecosystems.
Why AI changes data centre requirements
AI training and inference platforms can concentrate substantial compute into a small number of racks. GPU servers draw more power and produce more heat than many traditional enterprise systems. They also rely on fast east-west networking, large storage throughput and carefully managed data pipelines. As a result, an available rack is not necessarily an AI-ready rack. The facility must support the required electrical density, cooling method, equipment weight, network design and operational procedures. Capacity planning should begin with the exact hardware specification and workload profile.
Power density and cooling
AI racks may require tens of kilowatts and, in some architectures, substantially more. High-density deployments can need larger electrical feeds, busway capacity, specialised rack PDUs and advanced monitoring. Air cooling may support some systems, while direct-to-chip liquid cooling or other technologies are required for denser configurations. Confirm supply and return temperatures, coolant distribution, leak detection and responsibility for customer-side components. Major providers including NEXTDC and Equinix are expanding AI-ready capacity, but capability differs by facility, hall and deployment size.
Networking for GPU clusters
AI clusters exchange large volumes of data between accelerators, storage and orchestration systems. Network design can affect training time and hardware utilisation. Depending on the platform, customers may use high-speed Ethernet, InfiniBand or specialised fabrics with strict latency and topology requirements. External connectivity also matters for data ingestion, cloud bursting, model distribution and user access. Ask about diverse fibre paths, carrier ecosystems, cloud on-ramps and the ability to connect multiple racks without creating bottlenecks.
Colocation compared with cloud AI
Public cloud provides rapid access to managed AI services and rented accelerators without buying hardware. Colocation gives customers more control over server choice, utilisation, data placement and long-term architecture. It may be attractive for stable, heavily used workloads or specialised systems, but it requires capital, engineering and lifecycle management. Many organisations use both: cloud for experimentation and elastic demand, with colocated infrastructure for predictable production workloads, sensitive data or sustained utilisation. Total cost should include power, networking, staff, hardware refresh and downtime risk.
How to select an AI colocation site
Provide the provider with rack dimensions, weight, peak kW, cooling interface, network ports and growth plan. Confirm whether capacity is available now and whether future expansion can be reserved. Review commissioning, remote hands, spare-parts storage and vendor access. Ask how the facility monitors power, temperature and liquid systems and how incidents are escalated. Consider proximity to data sources, users, cloud platforms and technical staff. AI infrastructure is a complete system; selecting a site on power density alone can overlook network, cooling and operational constraints.
Frequently asked questions
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