With digital transformation accelerating across industries and the rise of power-intensive AI applications, global demand for data services is growing exponentially.
THE International Energy Agency indicates that data centers represent approximately 1% of global electricity demand. By 2030, data centers are expected to reach 35 gigawatts of electricity consumption per year, up from 17 gigawatts in 2022, according to McKinsey.
As explained Marc GarnerSenior Vice President of Secure Power Europe at Schneider ElectricAI has become a transformative force, changing the way we process, analyze and use data.
“As the AI market is expected to reach a staggering level $407 billion by 2027this technology continues to revolutionize many industries, with annual revenue expected growth rate of 37.3% between 2023 and 2030», he tells us.
“The AI market has the potential to grow even further, thanks to the rise of generative AI (Gen AI). 97% of business owners I believe ChatGPT will benefit their organizations, through uses such as streamlining communications, generating website copy, or translating information, but growing adoption will undoubtedly require investment and infrastructure more important than ever for AI-based solutions.
Meeting the demands of this new AI-powered world brings challenges.
“Data centers are the critical infrastructure that supports the AI ecosystem,” says Garner. “Although AI requires large amounts of energy, AI-driven data analysis can help bring data centers closer to net zero and play a positive role in solving the sustainability challenge. »
Here Garner explores the four key AI attributes and trends that underpin data center physical infrastructure challenges: power, racks, cooling, and software management.
How to deal with the rise in power-hungry AI applications
As Garner explains, power, cooling, racks and physical infrastructure are critical to the success of a data center.
“Storing and processing data to train machine learning (ML) and large language models (LLM) leads to a steady increase in energy consumption,” he says. “For example, researchers estimate that the creation of GPT-3 consumed 1,287 megawatt hours of electricity and generated 552 tonnes of CO2 — the equivalent of 123 gasoline-powered passenger vehicles driven for a year. Additionally, data centers are adopting high-density racks capable of accommodating a greater number of servers in a smaller space, further increasing power requirements.
“So how can we meet these growing demands for AI power, while minimizing its impact on the planet? Data centers are continually evolving to meet the growing power demands of AI clusters. Improving power distribution systems and energy efficiency within data centers helps minimize losses and ensures that energy is delivered to servers in the most efficient manner possible. When operators design and manage data centers, they must focus on energy-efficient hardware and software, while diversifying energy sources to provide the safe and abundant energy AI needs to thrive.
“Additions such as advanced power distribution units (PDUs), intelligent management and high-efficiency power systems, along with renewable energy sources, enable data centers to reduce both energy costs and carbon emissions. However, the extreme power densities of AI training servers can create additional issues beyond power consumption: cooling, for example, can also create complex challenges for operators.
