How to Build a Data Center for AI Workloads

Artificial intelligence is reshaping the data center industry from the ground up — literally. The facilities that house AI training clusters and inference workloads look fundamentally different from the enterprise data centers built over the past two decades. Higher power densities, liquid cooling infrastructure, heavier structural loads, and specialized electrical systems all change how these buildings are designed and constructed.

If you are planning to build a data center for AI workloads, you need to understand these differences before the first line is drawn on the design documents. For a broad overview of the data center construction process, start with our guide on how to build a data center. This article focuses specifically on what changes when AI is the primary workload.

How AI Data Centers Differ from Traditional Facilities

The core difference comes down to power density. A traditional enterprise data center operates at 6-8 kW per rack. A modern AI training facility operates at 30-80 kW per rack, with some GPU-dense configurations pushing beyond 100 kW per rack. That single metric — power density — cascades through every system in the building.

Power Density Comparison

When every rack in a data hall draws 50-80 kW instead of 6-8 kW, you need roughly ten times the power distribution infrastructure, ten times the cooling capacity, and significantly heavier structural support — all in the same physical footprint.

Electrical Infrastructure for AI

The electrical system is the single largest area of change in AI data center construction. Traditional data centers distribute power at 480V through PDUs and power whips to individual racks. AI facilities often require:

Larger transformers and switchgear: Higher power density per building means larger utility feeds. A 10MW traditional data hall might become a 40-80MW AI hall in the same footprint, requiring proportionally larger electrical infrastructure.

Higher-capacity distribution: Bus duct systems replace traditional cable-and-conduit distribution in many AI facilities because they can carry more current in less space and are faster to install.

Larger conductor sizes: The sheer amperage required to feed high-density racks means larger wire gauges, bigger conduit, and more copper throughout the facility.

Backup power scaling: Generator capacity must match the higher power draw. A 50MW AI facility might need 20+ generators compared to 5-6 for a comparable traditional facility.

The construction implication is clear: you need more electricians with experience in medium- and high-voltage systems, and the electrical scope will take longer and cost more than a traditional build.

Liquid Cooling: The Defining Feature of AI Data Centers

Air cooling, the dominant approach for decades, cannot efficiently reject 50-80 kW per rack. Liquid cooling is not optional for AI facilities — it is a requirement. There are three primary approaches:

Direct-to-Chip (Cold Plate) Cooling

Water or a dielectric fluid is pumped through cold plates mounted directly on GPU and CPU packages. This is the most common approach for new AI deployments. It requires:

• Chilled water distribution piping to every rack position
• Coolant distribution units (CDUs) on the data hall floor or in adjacent mechanical rooms
• Leak detection systems throughout the data hall
• Precision piping with tight tolerances

Rear-Door Heat Exchangers

A heat exchanger is mounted on the back of each rack, with chilled water flowing through it to remove heat from exhaust air. This is a simpler retrofit option but less efficient for the highest density deployments.

Immersion Cooling

Servers are submerged in a dielectric fluid that absorbs heat directly. This approach handles the highest densities but requires specialized tanks instead of traditional racks and represents the biggest departure from conventional data center construction.

Construction Workforce Implications

Liquid cooling transforms the mechanical scope of data center construction. A traditional air-cooled facility might have limited piping — mainly to support perimeter cooling units. An AI facility with direct-to-chip cooling has piping running to every single rack position, with hundreds or thousands of connections that must be leak-free.

This means you need significantly more pipefitters on an AI data center project than on a traditional build. The ratio of pipefitter hours to electrician hours shifts substantially — from perhaps 1:3 in a traditional facility to 1:1.5 or even 1:1 in a liquid-cooled AI facility.

Structural Considerations

AI servers are heavy. A fully loaded AI rack with eight GPUs, associated networking, and power distribution can weigh 3,000-4,000 pounds or more. Traditional server racks typically weigh 1,500-2,500 pounds fully loaded.

This has direct structural implications:

Heavier floor systems: Raised floors may not be practical for the heaviest AI configurations. Many AI data centers use slab-on-grade designs with reinforced concrete capable of handling 300-500 pounds per square foot.

Larger structural steel: Column spacing, beam sizing, and connection details must all account for the concentrated loads from high-density racks.

Foundation design: Greater building loads drive larger foundations, which increases concrete and reinforcing steel quantities and extends the site work phase.

Equipment rigging: Getting 4,000-pound racks into position requires careful rigging planning and potentially larger overhead crane capacity during construction and operations.

The structural scope increase means more ironworkers during the steel erection phase and more concrete work during foundations — both trades that are already in high demand across hyperscale data center construction.

Design Considerations Unique to AI Facilities

Beyond the core infrastructure differences, AI data centers have several design considerations that traditional facilities may not address:

Network topology: AI training clusters require ultra-low-latency, high-bandwidth interconnection between GPUs. This means dense fiber and copper cabling within the data hall, often with dedicated network rooms and structured cabling systems that are more complex than traditional deployments.

Row and rack layout: AI clusters often have specific rack adjacency requirements to minimize network latency. The data hall layout must accommodate these requirements, which may constrain the physical design in ways that traditional data centers do not experience.

Heat rejection at scale: Even with liquid cooling handling the majority of the heat load, the facility still needs to reject massive amounts of heat to the atmosphere. Cooling tower or dry cooler farms for AI facilities are substantially larger than those for traditional data centers.

Redundancy philosophy: Some AI operators accept a different redundancy model than traditional enterprise or colocation facilities. Training workloads may tolerate brief interruptions that a financial services or healthcare workload would not. This can simplify some systems but requires careful alignment with the operator's availability requirements.

Construction Timeline and Cost Implications

AI data centers generally cost more per MW and take longer to build than traditional facilities of the same IT capacity:

These are generalizations — actual costs and timelines vary significantly based on design, location, and market conditions. But the directional impact is clear: AI facilities require more of everything.

Workforce Planning for AI Data Center Construction

The workforce requirements for AI data center construction differ from traditional builds in several important ways:

More mechanical trades: The liquid cooling scope drives a significant increase in pipefitter and plumber hours. Make sure your staffing plan accounts for this shift.

Specialized skills: Liquid cooling piping requires clean-room-grade welding and brazing in many cases. Not every pipefitter has this experience.

Longer peak durations: The expanded MEP scope means the peak workforce period is longer, which compounds staffing challenges in constrained labor markets.

Higher overall headcount: A 50MW AI facility might require 20-30% more peak workers than a 50MW traditional facility.

Commissioning complexity: Testing liquid cooling systems, verifying leak-tightness across thousands of connections, and integrating cooling controls with IT load management all require specialized commissioning expertise.

Building for the AI Era

The data center industry is in the early innings of the AI infrastructure buildout. The facilities being designed and constructed today will need to support AI workloads that are growing exponentially in scale and complexity. Building these facilities correctly requires understanding the fundamental differences in power, cooling, structural, and workforce requirements compared to traditional data centers.

Cortex Construct provides the specialized tradespeople needed for AI data center construction — from the electricians who install high-capacity power distribution to the pipefitters who build liquid cooling systems. If you are building for AI and need a workforce partner who understands these unique requirements, contact our team to discuss your project.