High-density AI environments call for a fundamentally different approach to data center design.
Driven by rising AI rack densities, data centers are slowly but surely shifting to liquid cooling as standard. And with power availability increasingly becoming the primary constraint for site selection, data center operators are also integrating renewable energy and Battery Energy Storage Systems (BESS) into their facilities.
As data centers evolve to accommodate these new realities, their design and operational strategies must change with the times, too. This shift demands a more integrated approach, one that treats power, cooling, and infrastructure not as separate concerns but as interdependent systems that must be planned together from the outset.
Beyond conventional design
New operators frequently underestimate the operational complexity of high-density GPU environments, treating an AI deployment as simply a scaled-up version of a conventional facility. In practice, AI hardware represents a fundamentally different type of infrastructure that necessitates a distinct design and operational approach. Power density, cooling architecture, structural capacity, and operational resilience are deeply interconnected and must be designed as an integrated system. Projects that fail to address this holistically often encounter challenges during deployment or expansion.
For instance, designs are often drawn up based on theoretical peak power ratings rather than real-world load behavior. This means transient spikes and uneven thermal distribution are typically not taken into consideration, which can be a serious issue given the high power draw of GPUs. Inadequate attention to upstream electrical redundancy, voltage stability, floor loading, and serviceability can also significantly impact long-term reliability and scalability.
In the same vein, liquid cooling is often evaluated primarily on thermal performance, while long-term operational factors receive less attention. Yet considerations such as coolant quality management, leak detection strategies, maintenance workflows, and compatibility with existing facility standards are critical to ensuring reliability over time. Without proper planning for these lifecycle factors, liquid cooling can introduce operational risk instead of delivering its intended benefits.
Integration over isolation
Within a high-density data center, performance and efficiency are driven by system-level integration, not individual components. Electrical infrastructure, cooling systems, control logic, and monitoring platforms must be engineered to operate as a unified ecosystem rather than as discrete systems.
Consider the deployment of the Nvidia GB300. With racks that can exceed 70–120kW, cooling is critical. Yet high-density fiber optics can create thermal zoning and layout constraints that are often overlooked in initial planning. Direct-to-chip cooling also demands treated coolant, dual-loop redundancy, and strict operational control. Each of these elements must work in concert; optimizing one in isolation can undermine the others.
This interconnectedness makes it essential to begin with a clear understanding of target workloads and growth trajectories. Unlike traditional data center environments that remain relatively static, AI compute platforms evolve rapidly, often outpacing typical retrofit timeframes. What works today may be inadequate within a few years as rack densities increase and cooling requirements shift.
Operators who recognize this can design infrastructure with built-in adaptability, whether through modular power distribution, flexible cooling pathways, or spatial layouts that anticipate higher densities. This approach helps avoid costly overhauls and maintains the flexibility to accommodate next-generation hardware. In AI environments, future-proofing is not optional; it is foundational to long-term success.
Staying ahead
The AI landscape is evolving at a relentless pace. New GPU architectures, shifting power requirements, and advances in cooling technology mean that best practices today may be outdated within months. For operators and designers, staying current is not just advantageous; it is essential to delivering infrastructure that remains viable over its intended lifespan.
Equally important is the recognition that successful AI deployments are the result of early, coordinated collaboration across disciplines. When electrical, mechanical, and structural teams work in silos, inefficiencies and design conflicts are almost inevitable. A more integrated approach, where stakeholders align from the outset, leads to facilities that are more resilient, efficient, and adaptable.
This philosophy underpins how DongFong Technology approaches every project, whether data center design and construction, GB300 AI deployments, or national-level integration initiatives. To ensure its consultants and designers stay at the forefront, the company maintains monthly internal reviews of the latest GPU developments while collaborating directly with Nvidia ecosystem partners.
With operational data centers in Taipei and Taichung delivering colocation, dark fiber, and enterprise network services, DongFong Technology offers end-to-end AI and data center engineering, from design and construction through to commissioning and operations.
Planning your next infrastructure project? A conversation with DongFong Technology may be the place to start.
****This advertorial first appeared in Issue 11 of w.media’s Cloud & Datacenters magazine.