Limiting AI's High Power Consumption Habits
Artificial intelligence (AI) is rapidly growing and consuming unprecedented amounts of electricity and water, according to a new analysis from Accenture. Over the next five years, AI data centers could consume up to 612 terawatt-hours, equivalent to Canada's total annual electricity consumption, and more than 3 billion cubic meters of water annually, more than the total annual freshwater withdrawals of countries like Norway or Sweden.
To address this issue, two key strategies are emerging: AI governance and energy-aware AI scheduling.
AI Governance through Standardized Metrics and Transparency
Establishing standardized, comprehensive metrics for AI energy and environmental footprints—including model training, inference, and infrastructure—allows better tracking and management of AI’s growing energy demands. This transparency enables policymakers, grid operators, and organizations to make informed decisions and implement regulations or strategies to reduce consumption and carbon emissions.
Governance efforts coordinated by agencies such as the DOE, NIST, and EPA can implement uniform data reporting and integrate these metrics into energy planning, improving energy use visibility across AI data centers.
Energy-Aware AI Scheduling to Optimize Power Use
Adaptive scheduling shifts AI workloads to times when power is cleanest and cheapest, such as off-peak hours, thereby reducing peak energy demand and associated emissions. Scheduling can include dynamic scaling and smart load balancing, where AI infrastructure matches energy use to AI workloads, essentially scaling energy consumption in proportion to workload demands.
Example: The MIT and Northeastern-developed software "Clover" dynamically adjusts AI model complexity (e.g., using lower-quality, less power-intensive models during high grid demand) to save energy without significantly compromising output quality.
Combined Effects of Governance and Scheduling
Governance frameworks standardize energy consumption measurements and reporting, creating the necessary data to support energy-aware scheduling policies. Energy-aware scheduling uses these insights to deploy AI tasks in an energy-proportional manner, cutting down waste and avoiding high-carbon periods, which enhances sustainability.
Placing data centers near underused renewable resources combined with energy storage improves utilization and reduces energy waste.
Additional Strategies Related to AI Governance and Scheduling
Governance encourages the use of right-sized AI models rather than large, resource-heavy models by promoting task-specific AI that consumes less power. Deployment of AI at the edge reduces reliance on cloud data centers, lowering overall data center energy footprint.
Regulatory efforts, while sometimes controversial, may focus on infrastructure improvements and streamlining data center permits to balance AI growth with energy and environmental demands.
In summary, AI governance provides the framework for transparency, accountability, and standardized measurement essential for managing AI’s resource usage, while energy-aware AI scheduling enables intelligent timing and scaling of workloads that optimize energy consumption based on cost and environmental factors. Together, these approaches help mitigate the substantial and rising energy demands of AI data centers and reduce their environmental impact.
The integration of AI governance, with standardized metrics and transparency, facilitates informed decision-making regarding AI consumption and carbon emissions, making it possible for policymakers to implement strategies reducing energy use and emissions.
Energy-aware AI scheduling, by adjusting workloads to off-peak hours and dynamically scaling energy consumption, helps minimize peak energy demand, promoting sustainability and reducing emissions associated with AI data centers.
