Schneider Electric has launched an industry-first guide to addressing new physical infrastructure design challenges for data centers to support the shift in artificial intelligence (AI)-driven workloads, setting the gold standard for AI-optimized data center design.
Titled “The AI Disruption: Challenges and Guidance for Data Center Design,” this groundbreaking document provides invaluable insights and acts as a comprehensive blueprint for organizations seeking to leverage AI to its fullest potential 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. White Paper 110 is available for download here.
In an era where AI is reshaping industries and redefining competitiveness, Schneider Electric’s latest white paper paves the way for businesses to design data centers that are not just capable of supporting AI, but fully optimized for it. The white paper introduces innovative concepts and best practices, positioning Schneider Electric as a frontrunner in the evolution of data center infrastructure.
“As AI continues to advance, it places unique demands on data center design and management. To address these challenges, it’s important to consider several key attributes and trends of AI workloads that impact both new and existing data centers,” said Pankaj Sharma, Executive Vice President, Secure Power Division and Data Center Business at Schneider Electric. “AI applications, especially training clusters, are highly compute-intensive and require large amounts of processing power provided by GPUs or specialized AI accelerators. This puts a significant strain on the power and cooling infrastructure of data centers. And as energy costs rise and environmental concerns grow, data centers must focus on energy-efficient hardware, such as high-efficiency power and cooling systems, and renewable power sources to help reduce operational costs and carbon footprint.”
This new blueprint for organizations seeking to leverage AI to its fullest potential within their data centers, has received welcome support from customers.
“The AI market is fast-growing and we believe it will become a fundamental technology for enterprises to unlock outcomes faster and significantly improve productivity,” said Evan Sparks, chief product officer for Artificial Intelligence, at Hewlett Packard Enterprise. “As AI becomes a dominant workload in the data center, organizations need to start thinking intentionally about designing a full stack to solve their AI problems. We are already seeing massive demand for AI compute accelerators, but balancing this with the right level of fabric and storage and enabling this scale requires well-designed software platforms. To address this, enterprises should look to solutions such as specialized machine learning development and data management software that provide visibility into data usage and ensure data is safe and reliable before deploying. Together with implementing end-to-end data center solutions that are designed to deliver sustainable computing, we can enable our customers to successfully design and deploy AI, and do so responsibly.”
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 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.