Schneider Electric: AI Design is the Future of Data Centers

Schneider Electric has launched the industry's first guide to address new infrastructure design challenges in data centers to support the transformation of AI-driven workloads. The guide, titled "AI Disruption: Challenges and Guidance for Data Center Design," provides valuable insights as organizations harness the full potential of AI in their data centers. It includes a forward-looking perspective on emerging technologies to support high-density AI clusters. It is no secret that AI has brought significant changes to data center design and operations. As AI applications become more prevalent and influential in critical industries, the demand for processing power continues to rise. Schneider Electric states that as the proportion of AI workloads in data centers increases, data centers are shifting towards higher rack power densities. AI startups, enterprises, colocation providers, and internet giants must now consider the impact of these densities on the design and management of data center physical infrastructure. The guide explains the relevant attributes and trends of AI workloads and provides recommendations for addressing these challenges in each physical infrastructure category, including power, cooling, racks, and software management. It emphasizes that data centers must adapt to evolving power requirements to stay ahead and effectively meet the demands of AI-driven applications. According to predictions, the compound annual growth rate of AI workloads will reach 26% to 36% by 2028, leading to an increased demand for power in existing and new data centers. Schneider Electric's guide covers four physical infrastructure categories - power, cooling, racks, and software tools - and provides several key considerations for addressing this projected energy demand. In an era where AI is reshaping industries and defining digital transformation, Schneider Electric's guide aims to pave the way for data centers that are not only capable of supporting AI but also fully optimized for it. "With the continuous development of AI, it presents unique requirements for the design and management of data centers. To address these challenges, it is important to consider several key attributes and trends of AI workloads that impact new and existing data centers," said Pankaj Sharma, Executive Vice President of Secure Power Division and Data Center Business at Schneider Electric. "AI applications, especially training clusters, are highly computationally intensive and require significant processing power provided by GPUs or dedicated AI accelerators. This puts significant pressure on the power and cooling infrastructure of data centers. With rising energy costs and increasing environmental concerns, data centers must focus on energy-efficient hardware such as high-efficiency power supplies and cooling systems, as well as renewable energy, to help reduce operational costs and carbon footprint." Schneider Electric states that this new blueprint, aimed at organizations looking to fully leverage the potential of AI in their data centers, has received enthusiastic support from customers. The guide explores the key intersections between AI and data center infrastructure, addressing challenges in power, cooling, racks, and software management. Similarly, the guide provides recommendations for assessing and supporting high rack power densities for AI training servers and specifies the requirements for AI servers that need high power, cooling pipelines, and a large number of network cables. "The AI market is growing rapidly, and we believe it will become a fundamental technology for enterprises to achieve business goals faster and significantly improve productivity," said Evan Sparks, Chief Product Officer of Artificial Intelligence at Hewlett Packard. "As AI becomes the dominant workload in data centers, businesses need to consciously consider designing a complete stack to address AI challenges. We have seen a tremendous demand for AI computing accelerators, but balancing this with the right fabric and storage levels and achieving this scale requires well-designed software platforms." "To address this issue, businesses should seek specialized solutions such as machine learning development and data management software, which provide visibility into data usage and ensure data is secure and reliable before deployment. By implementing end-to-end data center solutions aimed at providing sustainable computing capabilities, we can enable customers to successfully design and deploy AI and implement it responsibly."