AI Colocation Guide: What GPU Workloads Need That Standard Racks Can't Provide

The Power Density Gap

Traditional enterprise colocation was designed for a different era. Most facilities built in the 2000s and 2010s provisioned 5-8kW per rack — plenty for general-purpose servers, storage arrays, and networking equipment. But AI has changed everything.

A single NVIDIA DGX H100 system draws 10.2kW. A fully loaded rack of GPU servers can easily exceed 40kW, with some configurations pushing past 100kW. This isn't a minor infrastructure upgrade — it's a fundamental rethinking of how data centers are built.

Why Standard Colocation Fails AI Workloads

When enterprises try to deploy AI infrastructure in traditional colocation, they hit three critical walls:

**Power Density**: Most facilities can't deliver more than 10-15kW to a single rack without major infrastructure modifications. Even if they claim "high density" support, the actual available capacity may be limited.

**Cooling Capacity**: Air cooling reaches its practical limits around 30-40kW per rack. Beyond that, you need liquid cooling — either direct-to-chip (DLC) or rear-door heat exchangers (RDHx). Most colocation providers simply don't have this infrastructure.

**Power Availability**: AI clusters are hungry. A 100-rack GPU deployment might need 4-10MW of dedicated power. Facilities designed for traditional workloads may not have the electrical infrastructure to support this concentration.

Liquid Cooling: No Longer Optional

For deployments exceeding 40kW per rack, liquid cooling isn't a luxury — it's a requirement. The two primary approaches are:

**Direct-to-Chip (DLC)**: Coolant flows directly to cold plates mounted on GPUs and CPUs, providing the most efficient heat removal. Required for the highest-density configurations.

**Rear-Door Heat Exchangers (RDHx)**: A liquid-cooled door replaces the rear of the rack, capturing heat as air exits. Effective for 40-80kW deployments without modifying server hardware.

JustColo's Princeton facility is built liquid-cooling-ready from day one, supporting both DLC and RDHx configurations for racks exceeding 100kW.

Choosing the Right AI Colocation Partner

When evaluating facilities for AI workloads, ask these questions:

What is the maximum power density per rack — and how many racks can support that density?
What liquid cooling options are available, and what's the lead time for deployment?
What's the total campus power capacity, and how much is available for new deployments?
What connectivity options exist for hybrid cloud AI workflows?

The answers will quickly separate facilities built for the AI era from those retrofitting legacy infrastructure.

Princeton: Built for AI from Day One

JustColo's Princeton facility opens in January 2027 with native support for high-density AI workloads: 15-40kW air-cooled racks, liquid cooling infrastructure for 100kW+ configurations, and direct fiber connectivity to major cloud on-ramps.

Ready to discuss your AI infrastructure requirements? Let's talk.