By Prince Okafor

Energy management and automation company, Schneider Electric, has announced guide to tackle challenges of designing physical infrastructure for data centers in the era of Artificial Intelligence, AI-driven workloads.

This was contained in a document, titled “The AI Disruption: Challenges and Guidance for Data Center Design,” made available to Vanguard.

It serves as a comprehensive blueprint for organizations looking to harness the power of AI within their data centers.

Vanguard gathered that AI’s rapid growth is driving increased power demands in data centers.

According to the Executive Vice President of Schneider Electric’s Secure Power Division and Data Center Business, Pankaj Sharma, AI’s advancement 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.

“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.” 

Also, chief product officer for Artificial Intelligence, Hewlett Packard Enterprise, Evan Sparks, said: “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.

“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.”