Schneider Electric has introduced an industry-first guide to address new infrastructure design challenges for data centres to support the shift in AI-driven workloads.
The guide, The AI Disruption: Challenges and Guidance for Data Centre Design, provides invaluable insights and acts as a blueprint for organisations seeking to leverage AI to its fullest potential within their data centres. This includes a forward-looking view of emerging technologies to support high density AI clusters in the future.
It is no secret that AI disruption has led to significant changes and challenges in data centre design and operation. As AI applications have become more prevalent and impactful on key industry sectors, so has the demand for processing power.
Data centre sector shifts more towards AI
AI is becoming a larger workload percentage within data centres, representing a shift to higher rack power densities. AI start-ups, enterprises, colocation providers and internet giants must now consider the impact of these densities on the design and management of the data centre physical infrastructure, according to Schneider Electric.
The guide explains relevant attributes and trends of AI workloads and offers advice to address these challenges for each physical infrastructure category including power, cooling, racks and software management. It makes clear that, in order to stay ahead of the curve, data centres must adapt to meet the evolving power needs of AI-driven applications effectively.
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 centres.
Servicing this projected energy demand involves several key considerations outlined in Schneider Electric’s guide, which addresses the four physical infrastructure categories – power, cooling, racks and software tools. In an era where AI is reshaping industries and defining digital transformation, Schneider Electric’s guide aims to pave the way for businesses to design data centres that are not just capable of supporting AI, but fully optimised for it.
“As AI continues to advance, it places unique demands on data centre 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 centres,” says Pankaj Sharma, Executive Vice President of the Secure Power Division and Data Centre Business at Schneider Electric.
“AI applications, especially training clusters, are highly compute-intensive and require large amounts of processing power provided by GPUs or specialised AI accelerators. This puts a significant strain on the power and cooling infrastructure of data centres. And as energy costs rise and environmental concerns grow, data centres 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.”
Unlocking AI and its full potential
This new blueprint for organisations seeking to leverage AI to its fullest potential within their data centres, has received welcome support from customers, according to Schneider Electric. The guide explores the critical intersections of AI and data centre infrastructure, addressing key considerations such as AI attributes and trends that underpin physical infrastructure challenges in power, cooling, racks and software management.
Similarly, it offers recommendations for assessing and supporting the extreme rack power densities of AI training servers and proposing specifications to accommodate AI servers that require high power, cooling manifolds and piping and large number of network cables.
“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,” says Evan Sparks, Chief Product Officer for AI at Hewlett Packard Enterprise.
“As AI becomes a dominant workload in the data centre, organisations 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 specialised 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 centre solutions that are designed to deliver sustainable computing, we can enable our customers to successfully design and deploy AI, and do so responsibly.”
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