Artificial Intelligence (AI) is no longer a concept confined to science fiction films; it's a reality that is rapidly transforming our world.
From virtual assistants and self-driving cars to personalized medicine and predictive analytics, AI is revolutionizing industries and reshaping the way we live and work. As AI continues to advance and permeate every aspect of our lives, the demand for computing power and data storage is skyrocketing. This is where data centers come into play. As AI continues to transform industries worldwide, the demand for specialized AI data centers is on the rise. These next-generation facilities are purpose-built to handle the unique requirements of AI workloads. This blog examines the increasing data center power density demands driven by AI.
AI is the key driver of data center capacity demand.
According to McKinsey's report2 suggests that demand for AI-ready data center capacity will rise at an average rate of 33 percent a year between 2023 and 2030 in a midrange scenario. This means that around 70 percent of total demand for data center capacity will be for data centers equipped to host advanced-AI workloads by 2030. Gen AI, currently the fastest-growing advanced-AI use case, will account for around 40 percent of the total.
[Image courtesy Mckinsey & Company]
The companies driving most of the additional demand for AI-ready data centers include cloud service providers (CSPs) such as Amazon Web Services (AWS), Google Cloud, Microsoft Azure, and Baidu. This is because these hyperscalers require capacity to operate large foundational models like Google’s Gemini or to host models developed by AI companies such as OpenAI’s ChatGPT.
Most other companies tend to use off-the-shelf models hosted on public cloud platforms or modify them to suit their needs. However, as the technology matures, more companies are likely to build and train their own models based on their proprietary data.
The Power of AI Depends on Power
Unsurprisingly, AI applications are very power-intensive. Particularly, deep learning models lead to higher processing requirements for data centers because training and executing AI models relies on substantial computational power. Running these applications demands advanced hardware such as GPUs (specialized electronic circuits that accelerate graphics and image rendering) and TPUs (circuits designed to accelerate AI and machine learning workloads).
Traditional data centers are designed with 5-to-10 kilowatts per rack as an average density; the advent of AI now requires 60 or more kilowatts per rack. Moreover, AI applications generate far more data than other types of workloads — which requires significant amounts of data center capacity.
New data centers must be built with a great deal more power density; that’s one part of enabling AI. Current data centers are adapting to these changes, increasing their capacities by implementing optimized interconnection, compute and storage solutions — something some legacy and most on-premises data centers would have trouble accomplishing at the scale needed to keep up with the latest tools.
Energy-intensive GPUs and TPUs give off so much heat that enhanced environmental controls, including liquid cooling solutions, can be required. This issue of heat is both a technical consideration and an environmental one.
[Image courtesy EPRI]
AI – especially AI model training – is much more energy-intensive than the applications that drove data center growth over the past two decades, according to a report from energy R&D specialist EPRI.1
The AI-Ready Data Center
Providing the infrastructure to support intense AI workloads is a critical first step in a modern IT solution. However, not all third-party data centers offer the same level of capabilities, innovation, expertise and service.
As a bare minimum, an AI-ready data center should offer scalable space, power, cooling and connectivity. These offerings should be complemented by a strong customer focus and commitment to continual innovation to address evolving requirements. Additionally, third-party data centers should offer the expertise to guide businesses though the rapidly shifting AI landscape.
Finding the right provider with the indispensable combination of infrastructure, skill and a customer-first approach can ease daily operations, ensure a more positive data center experience and serve as a key differentiator as businesses continue to adapt to and integrate advanced technologies.
So far, we have discussed the growing market demand for AI-ready data centers capable of supporting workloads in line with the growth of AI demand.
Next, we will explain the role of data centers in supporting AI.
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References
1. Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption,
EPRI, May 28, 2024
2. AI power: Expanding data center capacity to meet growing demand, McKinsey & Company, Oct 24, 2024
