As the artificial intelligence (AI) wave reshapes industries from healthcare to finance, the infrastructure required to support this transformation is evolving at breakneck speed. At the core of this transformation lies a new breed of data center racks built specifically for AI chips — commonly referred to as “AI Racks”. These are not your average server enclosures. They are bigger, deeper, denser, and optimized for the intense power and cooling demands of modern compute-heavy AI workloads.
AI Chips Need Bigger, Deeper, Denser Racks
With the proliferation of Google TPUs, high-performance GPUs, and specialized AI accelerators, traditional server racks simply don’t make the cut anymore. AI workloads demand massive parallel processing capabilities, leading to dense configurations with upwards of tens of kilowatts per rack. In response, AI Racks are engineered with increased depth to accommodate longer cards and greater vertical space to support more compute nodes.
The rack density has reached new heights — sometimes exceeding 50 kW per rack — far surpassing the 5–10 kW densities of traditional enterprise racks. This exponential leap requires a fundamental redesign of power delivery, structural support, and, most critically, cooling.
Cooling the Beast: 80% Liquid, 20% Air
To combat thermal challenges, modern AI Racks such as those available from Vertiv’s 360 AI follow an emerging best-practice cooling model: 80% liquid cooling and 20% air cooling. The shift toward TCS (Thermal Control System) water loops is key. These closed-loop liquid cooling systems deliver cold water directly to the chip or cold plates, absorbing the bulk of the heat at its source.
The remaining 20% air cooling still plays a role in dissipating residual heat and maintaining ambient temperatures within the rack. However, it is no longer the frontline defense. As power densities rise, liquid cooling is not just preferred — it’s mandatory.
Neo Scalers and GPU-as-a-Service
An exciting trend accompanying this hardware evolution is the rise of “Neo Scalers” — cloud-native AI infrastructure providers that rent GPU clusters rather than sell traditional virtual machines. These firms cater specifically to machine learning startups, academic researchers, and enterprises lacking in-house AI infrastructure.
By leveraging AI Racks packed with GPUs, these providers offer on-demand access to high-performance hardware without long-term capital investment. This business model is accelerating AI experimentation and reducing time-to-market for AI-driven solutions.
Deployment Schedules and Timelines
Deploying AI Racks isn’t a plug-and-play process. Due to the high electrical and thermal loads, rollout timelines are more complex than traditional servers. A full deployment can take 6 to 12 months, including:
- Site retrofitting brownfield facilities for higher concentrated loads
- Upgraded power and cooling infrastructure
- Liquid cooling loop installation
- Integration with orchestration software and AI frameworks
Some hyperscalers and Neo Scalers have begun phased deployments — starting with small-scale pilot pods (2-5 racks) and scaling up to hundreds as workload demand increases and power/cooling baselines stabilize.
The Future: Beyond AI Chips to Quantum Divergence
Looking ahead, the design of AI Racks may soon face another inflection point — quantum divergence. While AI chips like TPUs and GPUs are optimized for parallel matrix operations, quantum processors rely on entirely different physical properties such as superposition and entanglement.
Quantum systems won’t just require different chips — they’ll demand a rethink of the entire rack and data center paradigm, including cryogenic cooling, quantum interconnects, and quantum error correction circuits. Though widespread quantum deployment is still on the horizon, forward-thinking data center architects are already anticipating how future AI+Quantum hybrid environments might co-exist.
Raised Floors Still Matter
Despite modern advances, the raised floor remains a staple in many data centers — especially those deploying high-density AI Racks. With concentrated loads increasing significantly, raised floors must now support 2,500+ pounds per rack, prompting a redesign in underfloor structural systems and airflow engineering.
Raised floors continue to provide flexibility for cooling distribution, cable management, and power routing, especially in hybrid environments where both traditional and AI workloads operate side-by-side.
AI Racks are not just scaled-up versions of traditional server racks — they are a new architectural standard purpose-built for the era of accelerated computing. With denser designs, hybrid cooling systems, GPU-as-a-Service models, and forward-looking quantum considerations, AI Racks are at the center of tomorrow’s data infrastructure.
Contact DCFT to learn about how you can start your AI raised floor project and future proof your data center.