Artificial intelligence (AI) is rewriting the rules of data center design. What were once incremental increases in density have become step changes—with racks moving from 5–10 kW to 30–60 kW and beyond.
That acceleration is exposing hard limits across the stack:
- Cabinets pushed beyond structural ratings, strained further by added weight from liquid manifolds, PDUs, and dense cabling.
- Air cooling at its ceiling, even with containment, requiring strategies that bridge into liquid and hybrid approaches.
- Legacy 208V distribution proving inefficient, driving up copper use, losses, and operating costs.
- Cabling congestion compounding risks, obstructing airflow, complicating service, and reducing long-term maintainability.
AI workloads don’t just raise power and cooling demands—they collapse the margin for error in data center design.
With decades of experience enabling high-density environments, Chatsworth Products (CPI) has seen one constant: designing for scalability starts at the cabinet. The choices made here set the trajectory for efficiency, reliability, and growth in the AI era.
The True Cost of Retrofitting
Retrofitting AI density is not just a matter of adding more power or cooling—it often means reworking the foundation of the environment. The result is rarely smooth and almost always more disruptive than planned.
What operators discover too late:
- Cabinet limitations force replacements. Legacy racks often can’t handle the asymmetric loads, liquid manifolds, or higher-capacity PDUs that AI demands. Swapping cabinets after deployment is invasive, costly, and nearly impossible without downtime.
- Power retrofits ripple through the stack. Upgrading from 208V to higher-voltage distribution means reengineering breakers, feeders, and PDUs—disrupting both white space and gray space simultaneously.
- Cooling changes rarely fit cleanly. Adding containment or liquid integration in live environments can sometimes lead to patchwork solutions, compromised airflow, and reduced serviceability.
- Uptime takes the hit. Each retrofit increases operational risk and slows the very thing AI investment is supposed to accelerate - time-to-market.
The true cost isn’t just measured in capex—it’s in resilience, agility, and lost opportunity. Designing for density up front eliminates these risks.
Designing AI Infrastructure That Scales
Infrastructure scalability depends on more than adding space or power at the facility level. In an AI-driven environment, a scalable infrastructure strategy should focus on four critical dimensions:
Cabinet Strength:
AI-era racks aren’t just heavier—they are rear-biased and more complex. Systems designed for 5–10 kW must now support 30–60 kW, plus the added mass of liquid-cooling manifolds, rear-door heat exchangers, dual PDUs, busway stabs, and cable bundles.
The risk isn’t just static overload. It’s long-term fatigue, torsional twist under asymmetric loads, and instability during moves or in seismic zones.
Designing for scale requires:
- Specifying both static and dynamic ratings, with headroom for growth.
- Planning native support for liquid distribution (mounting points, drip containment, QD clearance).
- Reserving space for PDUs and structured cable pathways that preserve airflow.
- Prioritizing integrated cabinet platforms that combine strength, cooling, power, and cable management in a modular, flexible system—streamlining deployment today while leaving room to adapt as hardware and density requirements evolve.
Handled this way, the right cabinet can become an enabler of density. Platforms like CPI’s , engineered with industry-leading load ratings and liquid-ready features, provide this headroom without forcing design trade-offs.
Cooling Flexibility
At 30–60 kW per rack, cooling is no longer just a facilities decision—it’s a business-critical strategy. And increasingly, the cabinet itself is the front line of that strategy.
Despite frequent claims, air cooling is not obsolete. The real inefficiency comes from uncontrolled air. Thermal recirculation inside cabinets remains the root cause of hot spots and wasted energy. That’s why the smartest designs start with cabinet-level airflow management. Every improvement in separation and sealing amplifies the effectiveness of any liquid system layered on top.
For AI, cooling is not a binary choice of “air or liquid.” Hybrid strategies are now indispensable. Direct-to-chip liquid can remove 70%+ of GPU/CPU heat, while air continues to cool memory modules, VRMs, and other board-level components. Done right, hybrid cooling avoids premature overhauls, enables cabinet-by-cabinet adoption, and protects ROI as workloads scale.
The key is to design for progression: air containment builds the foundation, hybrid extends the bridge, and liquid precision-cools the components that push density beyond air’s limits. That means:
- Optimizing airflow with cabinet-level accessories, vertical exhaust ducts, or aisle containment.
- Deploying factory-integrated, pretested hybrid solutions that simplify liquid adoption.
- Preserve headroom with cabinets designed to support manifolds, heavier loads, and scalable pathways, so liquid cooling can be added without structural retrofits.
—such as CPI’s ZetaFrame® Cabinet System with advanced airflow design and factory-integrated liquid-ready architecture—ensure operators can navigate today’s workloads while preparing for tomorrow.
Power Delivery
Traditionally, power strategies focused on the gray space—UPS, switchgear, and remote distribution.
But with individual AI racks drawing 30–60 kW or more, the cabinet has become the critical point of control. Facility-level sizing is no longer sufficient. The real risks—breaker trips, deployment delays, and stranded capacity—arise inside the rack, where available power and actual demand must be matched with precision.
Key design principles for scalable AI power:
- Get proactive. Visibility, monitoring, and remote control all depend on PDUs in the rack. Without outlet-level data and proactive management, operators are blind to the rapid, unpredictable spikes that define AI power demand. Intelligent PDUs paired with DCIM software turn power from a reactive problem into a holistic, active control plane.
- Raise the voltage. Moving from 208V to 240/415V or 480V three-phase reduces copper bulk, lowers conversion losses, and doubles usable power per circuit—critical for GPU-dense racks.
- Right-size redundancy. AI servers often use three or more power supplies, shifting redundancy from simple A/B feeds to N+1 or N+2 models that require more precise breaker sizing and load mapping.
- Consider the footprint. Compact, high-capacity PDUs preserve airflow and space for liquid manifolds at the rear of the cabinet, while flexible outlet configurations reduce the number of PDUs required.
Solutions like CPI’s —with high-power configurations, support for 4 outlet types in a single PDU, and slim form factors—show how cabinet-level design choices directly shape both capacity and thermal performance.
Cable Management
Cabling has become one of the most underestimated constraints in AI deployments. GPU clusters drive massive east–west traffic, adding dense fiber interconnects on top of complex copper and power cabling. Without discipline, the result is congestion that blocks airflow, complicates cooling, slows service, and threatens long-term reliability.
Designing for scale means treating cabling as critical infrastructure, not an afterthought:
- Match the cable to the manager. High-density fiber, copper, and power each require pathways engineered for their characteristics. Mismatched managers lead to wasted space, airflow obstructions, and premature wear.
- Protect bend radius. Fiber integrity depends on maintaining proper bend radius (typically ≥10x the cable’s outer diameter). Poor routing or tight turns increase attenuation, shorten cable life, and raise failure risk.
- Avoid pathway overload. Overstuffed trays and managers strain cables, restrict bend radius, and limit room for growth. Scalable, right-sized managers help preserve both airflow and cable integrity as densities increase.
- Use fiber-friendly accessories. Soft straps, radius guides, and modular managers prevent stress, crushing, and accidental disconnects—while allowing easy reconfiguration during moves, adds, or changes.
- Plan for growth and flexibility. Leave slack for future rerouting, and design with modular, expandable management systems that can scale with AI hardware refreshes.
Platforms like CPI’s , with scalable vertical/horizontal pathways and fiber-friendly accessories, illustrate how disciplined cabling design underpins both cooling efficiency and operational resilience in high-density AI environments.
Building the Right Foundation with CPI
Chatsworth Products provides AI-ready solutions that address the full spectrum of infrastructure requirements: cabinet strength, adaptable cooling, intelligent power distribution, and scalable cable management. The ZetaFrame® Cabinet System, combined with CPI’s power and cooling innovations, delivers a flexible platform for operators preparing for the next wave of compute-intensive workloads.
Ready to design for density from day one? and learn how early infrastructure decisions can unlock long-term scalability and efficiency.
