The Impact of AI and Other Computing on Data Center Power
Artificial Intelligence (AI) is not the sole culprit when it comes to the strain on data centers; data-driven processes are also contributing to the increased demand for power. As Kathe Pelletier, senior director of community programs at EDUCAUSE, points out, the collection of data from various systems is putting a significant strain on them. One of her key roles is to assist higher education CIOs in adapting to new technologies like generative AI.
With the surge in data volumes, there is a looming shortage of silicon to meet the demand for processors and memory chips. Deep Jariwala, a professor at the University of Pennsylvania, warns that the supply chains for silicon may soon be unable to keep up with the data generation rate. Energy consumption is also escalating, and certain regions in the United States may face energy shortages due to the increasing demands of computing systems.
Despite these challenges, Kellen believes that innovations in technology, possibly driven by AI applications, will help us adapt to the changing landscape.
RELATED: Learn how to prepare higher education infrastructure for AI.
Shaping a Sustainable Future with Artificial Intelligence
AI can play a crucial role in addressing resource shortages and rising costs. According to CDW, AI can optimize data center operations and uncover potential energy savings, particularly in higher education settings. CDW’s sustainability efforts emphasize prolonging equipment lifecycles and reducing water consumption in cooling systems.
The Columbia Climate School suggests several ways AI can contribute to sustainability efforts, including developing more accurate climate prediction models and monitoring sensitive environments for early detection of problems.
Market forces are also expected to influence energy consumption patterns in the future. As AI providers recoup their development costs, institutions may face increased fees. This could prompt a reevaluation of the cost-benefit ratio, leading to more energy-efficient practices.
Cost pressures may drive innovation in energy-saving technologies, such as smaller learning models and improved cooling systems. However, the timeline for these efficiencies to offset rising demand remains uncertain.
