Ann Keefe, Regional Director – UK and Ireland at Kingston Technology, takes a look at how data centres can prepare for the explosive demand of the AI revolution.

The advent of AI is transforming industries at a breakneck pace. McKinsey has said that if 2023 was the year the world discovered generative AI, 2024 would be the year that organisations truly began using and deriving business value from it. But this rapid expansion is placing tremendous pressure on enterprise data centres. As AI-driven applications become more pervasive, the demand for computational power has skyrocketed. This challenges the capacity and efficiency of existing infrastructure, which brings AI to the forefront with features such as Copilot, Recall, and AI-augmented camera and touchscreen capabilities. These innovations underscore the growing need for data centres to evolve to meet the burgeoning demands of AI workloads.

Windows 11: A catalyst for AI demand

Windows 11 has been a game-changer in the computing landscape, particularly with its deep integration of AI features that cater to both consumers and enterprises. The Copilot feature, for instance, serves as an AI-powered assistant that streamlines user interactions, making it easier to manage tasks, search for information, and automate routine processes. 

These AI-driven enhancements are not just incremental improvements; they represent a fundamental shift in how users interact with technology. While applications like Copilot will sit on Microsoft’s own servers, or on dedicated devices such as AI PCs, they will pave the way for other similar applications and contribute to the exponential growth of data processing requirements. AI models, especially those used in real-time applications, require substantial resources to function efficiently. 

Microsoft invests in infrastructure

Recognising the growing demand for AI-driven services, and the need to support its own technology developments, Microsoft has already announced plans to expand its UK data centre footprint significantly. The tech giant is investing £2.5 billion to expand its next generation of AI data centres – the single largest investment in the UK in its 40-year history. This expansion is not just about adding more servers; it’s about creating a robust and scalable infrastructure that can handle the complex computational needs of AI applications.

Microsoft’s strategy highlights a broader trend among hyperscalers who are racing to build out their data centre capabilities to keep pace with the rapid adoption of AI. However, simply adding more physical infrastructure is not enough. Data centres must also focus on optimising the performance of their existing resources to maximise efficiency and reduce costs.

Key strategies for boosting server capacity

As AI continues to drive up performance demands, data centres need to explore strategies that can enhance the capacity of their existing servers, and two of the most effective approaches are to increase the DRAM (Dynamic Random-Access Memory) capacity and better optimise storage solutions. 

AI workloads, particularly those involving large-scale machine learning models and real-time data processing, require substantial memory to operate efficiently. By increasing DRAM capacity, facilities can ensure their servers are better equipped to handle these increasingly memory-intensive tasks, particularly when it comes to faster data retrieval and processing, both of which are crucial for maintaining the performance of AI applications under heavy loads.

Moreover, modern AI models often operate on large datasets that need to be held in memory for rapid access. Without sufficient DRAM and HBM, both of which can work hand-in-hand to deliver flexibility, these operations can bottleneck, leading to slower processing times and reduced overall efficiency. Upgrading server DRAM can mitigate these issues and offers scalability which enables data centres to support more complex AI workloads without compromising on performance.

Storage is another critical factor, as AI applications generate and require access to vast amounts of data. By optimising storage solutions, such as SSD drives, data centres can improve data throughput and reduce latency, ensuring faster access to data needed for AI processing. This, combined with increased DRAM, can create a more resilient and responsive data centre infrastructure.

Support for robust, scalable infrastructure

Windows 11 is just the latest in a series of developments that are bringing AI closer to businesses and users. The more it is integrated into the applications we use every day, the more we need robust, scalable and efficient data centre infrastructure. Data centres must also look inward, optimising their existing resources to withstand the explosion of AI-driven workloads. 

By increasing DRAM capacity and expanding storage, data centres will be better positioned to enhance their performance and remain competitive in this new era of AI.