Schneider Electric Issues Blueprint for Optimizing Data Centers to Harness AI Power
New guide addresses physical infrastructure design challenges for data centers to support the shift in AI-driven workloads.
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Schneider Electric’s new "The AI Disruption: Challenges and Guidance for Data Center Design" report provides insights and acts as a comprehensive blueprint for organizations seeking to leverage AI within their data centers, including a forward-looking view of emerging technologies to support high density AI clusters in the future.
Artificial Intelligence disruption has brought about significant changes and challenges in data center design and operation. As AI applications have become more prevalent and impactful on industry sectors ranging from healthcare and finance to manufacturing, transportation, and entertainment, so too has the demand for processing power. Data centers must adapt to meet the evolving power needs of AI-driven applications effectively.
Pioneering the Future of Data Center Design
AI workloads are projected to grow at a compound annual growth rate (CAGR) of 26-36% by 2028, leading to increased power demand within existing and new data centers. Servicing this projected energy demand involves several key considerations outlined in the white paper, which addresses the four physical infrastructure categories -- power, cooling, racks, and software tools. The white paper is available for download here.
In an era where AI is reshaping industries and redefining competitiveness, Schneider Electric’s report paves the way for businesses to design data centers that are not just capable of supporting AI, but fully optimized for it.
Unlocking the Full Potential of AI
Schneider Electric's AI-Ready Data Center Guide explores the critical intersections of AI and data center infrastructure, addressing key considerations such as:
- Guidance on the four key AI attributes and trends that underpin physical infrastructure challenges in power, cooling, racks, and software management
- Recommendations for assessing and supporting the extreme rack power densities of AI training servers
- Guidance for achieving a successful transition from air cooling to liquid cooling to support the growing thermal design power (TDP) of AI workloads
- Proposed rack specifications to better accommodate AI servers that require high power, cooling manifolds and piping, and a large number of network cables
- Guidance on using data center infrastructure management (DCIM), electrical power management system (EPMS), and building management system (BMS) software for creating digital twins of the data center, operations, and asset management
- Future outlook of emerging technologies and design approaches to help address AI evolution