Global data centers are undergoing a major transformation to accommodate the high power consumption demands of AI tools on hardware, networks, energy, and cooling systems. Some companies are even building dedicated AI data centers to drive the development of their own AI technologies.
So, compared to traditional data center workloads, what requirements does AI technology place on data center infrastructure?
From CPU to GPU
One of the biggest challenges AI faces in data center environments is its heavy reliance on GPU-based computing. GPUs support AI models by handling massive amounts of concurrent computation. This is crucial for meeting the enormous computational demands of training and running AI models. Traditional CPUs may excel at sequential processing, but as a result, they are too slow to enable many AI models to achieve optimal performance.
All of this means that AI data centers must be equipped with a large number of GPUs, which operate at higher voltages, resulting in significantly increased energy consumption. Higher power means more heat, which presents new challenges for data center owners and operators who need to balance power demand, cooling efficiency, and cost control.
Because AI-enabled racks require six times more power than traditional racks, data center developers are increasingly prioritizing regions rich in renewable energy and with naturally cool climates. Regions like Canada and Iceland are ideal choices due to their abundant hydroelectric and geothermal resources, providing reliable and affordable power for high-density AI workloads.
However, location selection is always about striking the right balance. This focus on strategic location brings a trade-off: data center facilities may be built far from end users, thus requiring consideration of any potential impact on latency. For some data centers, this is a compromise—choosing hydroelectric and mild climate regions while investing in advanced cooling technologies, such as liquid cooling and direct chip cooling, to provide better heat dissipation and higher energy efficiency.
Network innovation fuels the ever-growing demand for AI
AI is placing increasing demands on server computing power as more and more data needs to travel between GPUs as quickly as possible.
AI-driven applications also require exponentially increasing bandwidth to efficiently process the massive amounts of data they need. Servers may require data transfer speeds of up to 100Gbps to ensure that AI tools and applications function properly. To achieve this, GPU computing providers must change how they select and build their network stacks. Components that these providers may have used for years will no longer be suitable and will require a new selection and development process.
Therefore, data center operators are investing in high-performance interconnects to accelerate data transfer between large numbers of computing nodes, such as GPU clusters and TPUs (Tensor Processing Units), all of which are crucial for efficiently training and running complex AI models. Investing in advanced network hardware that can provide higher throughput, greater reliability, and lower latency is equally important.
The Future of AI Data Centers
To maintain a leading position, everyone is striving to seize opportunities, and now, that opportunity is AI.
Technically, AI can run in any data center. However, the GPU-based computing demands of AI place higher demands on power and cooling, meaning that not all data centers are cost-optimized for AI operations. In a highly competitive industry with a strong need for AI innovation, the increased demands on AI workloads based in traditional data centers mean that costs can easily spiral upwards.
Managing these costs is a key consideration for any operator building data centers for AI. While many enterprises are willing to pay extra to run AI workloads, data center operators must find ways to offset these costs and avoid passing the extra expense entirely on to customers if they want to remain competitive.
