Schneider Electric: Data Centres Optimized For AI

Data Centres Optimized For AI

Schneider Electric issued a handbook on optimizing data centers for AI in a digital transformation era.

The report addresses AI workload concerns in physical infrastructure categories like as power, cooling, racks, and software management by highlighting key qualities and trends. It shows that data centers must adapt to AI-driven apps’ power demands to stay ahead.

To accommodate AI-driven workloads, Schneider Electric has developed an industry-first guidance for data center infrastructure architecture.

AI workloads are expected to climb 26-36% annually by 2028, increasing power requirements in current and future data centers.

The AI Disruption

Challenges and Guidance for Data Centre Design guide offers significant insights and serves as a template for organizations aiming to maximize AI’s potential in their data centers. This includes a look ahead at future technologies to support high-density AI clusters.

AI disruption has changed data center architecture and operation, as is well known. As AI applications gain popularity and effect on major industries, processing power requirement has increased.

AI workloads are increasing in data centers, requiring higher rack power densities. Schneider Electric advises AI start-ups, companies, colocation providers, and internet giants to examine how these densities will affect data center physical infrastructure design and administration.

Realizing AI’s potential

Schneider Electric’s advice covers power, cooling, racks, and software solutions to meet this predicted energy need. Schneider Electric’s handbook helps organizations create data centers that are completely optimized for AI in an era where AI is altering industries and defining digital transformation.

Schneider Electric reports that clients have welcomed the new plan for organizations to fully utilize AI in their data centers. The handbook addresses AI traits and trends that underlie physical infrastructure difficulties in power, cooling, racks, and software management at the intersections of AI and data center infrastructure.

Additionally, it recommends examining and supporting AI training servers’ extraordinary rack power density and providing standards for AI servers that demand high power, cooling manifolds and piping, and many network connections.

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