The rapid growth of AI is raising many concerns about its long-term impact on human society. In the short term, however, AI is having one clear impact: the demands it is putting on the energy grid. AI applications often use more than four times as much power per rack as typical enterprise applications, due in part to higher GPU processor requirements. AI servers need almost 10 times the power of traditional data servers.
AI energy consumption is rapidly increasing, with an annual growth rate between 26% and 36%. In four years, data center power consumption is predicted to triple, and by some estimates, AI could use more power in 2028 than the entire country of Iceland did in 2021.
While AI offers immense potential, managing its energy needs will be crucial for a sustainable future. Luckily there are things we can do both in the short and long term to ensure we can power these transformative systems.
Better Hardware And Software
Data centers and cloud service providers can take immediate steps to optimize energy usage, such as consolidating AI workloads onto fewer servers to minimize the number of active machines. Dynamic voltage and frequency scaling (DVFS) helps save energy by adjusting the voltage and frequency of processors based on workload demands. Capping power usage during the training and inference phases can reduce energy consumption by 12 to 15%.
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Researchers can use techniques such as model compression, quantization and efficient neural network architectures to create more efficient AI algorithms and models that require less energy to train and run. AI can be used to optimize data center cooling systems by predicting and adjusting to temperature fluctuations, improve power distribution and management, and forecast energy demand and supply to better balance the grid. The idea is to leverage AI’s capabilities to optimize energy consumption overall and offset its increased power requirements—in effect, using AI to solve the problem it created.
Policy Progress
Balancing AI progress with environmental sustainability will require careful planning and collaboration between policymakers, researchers, businesses and developers. For example, policymakers should provide localized subsidies to support the development of renewable energy sources.
