How to optimize AI while minimizing its carbon footprint

Artificial intelligence (AI) and generative AI (GenAI) offer a transformational promise to propel all industries and the global economy forward. Data is essential to the learning and decision-making capabilities of AI and, as such, demand on data processing is expected to grow significantly. AI can quickly and efficiently draw insights from enormous data sets, which can require immense computational power, making data centre and PC performance critical.

Challenges come with any breakthrough technology and the environmental footprint of AI is already a topic the industry is working to address. Training and running large AI models and workloads comsume energy and resources and this presents a difficult decision for businesses who seek to embrace this revolutionary technology and meet their environmental sustainability commitments. From our vantage point, neither of these commitments is slowing down.

In May 2023, * Gartner® stated in its 2023 CEO Survey: Grow Through Digitally Enabled Sustainability, (produced by Kristin Moyer and Mark Raskino, May 19, 2023): “most CEOs (94%) will increase or hold sustainability and ESG investments at similar levels to 2022.”¹ Then the spotlight shifted to AI and its enormous potential to drive efficiency in organizations. Dell Technologies research found that currently, 76% of IT decision-makers plan to increase their budgets to support GenAI use cases and 78% are excited about how an investment in AI can benefit their organizations.

While investing in sustainability and energy-intensive technology appears to be at odds with one another, investing in sustainability and AI does not have to be an 'either/or' decision. Technological progress is a prerequisite for companies seeking to meet ambitious climate goals. The best innovations can – and should – do both: advance our technological capacity while supporting more energy-efficient and sustainable futures.

There are smart and sustainable technology investments and practices to reduce the environmental footprint of AI, while, at the same time, allowing us to leverage AI to solve some of the world’s biggest challenges. Sustainability is integral to the success of AI technology and vice versa.

While AI requires significant compute power, it currently represents a small fraction of IT's global energy consumption. We expect this will change as more companies, governments and organizations harness AI to drive efficiency and productivity across their operations and teams.

To manage and even offset AI’s growing carbon footprint, greater control over data centre energy consumption is increasingly becoming a top priority. According to **IDC in its Which Circularity Criteria Are Driving IT Planning and Procurement?, report, May 2023, the number one sustainability priority for IT planning and procurement among IT decision-makers is reducing data centre energy consumption.

These practical solutions can help make this priority a reality:

Minimize AI’s carbon footprint through modern, energy-efficient servers and storage devices and environmentally responsible cooling methods, while powering data centres with renewable energy. Use PCs and other hardware that deliver energy efficiency and include sustainable materials, such as recycled, ocean-bound or bio-based plastics, low-carbon emissions aluminium, closed-loop materials and recycled packaging.

While some organizations will benefit from larger, general-purpose large language models (LLMs), many organizations only require domain- or enterprise-specific implementations. Right-sizing compute requirements and infrastructure can support greater data centre efficiency. And, flexible 'pay as you go' spending models help organizations save on data centre costs, while supporting sustainable IT infrastructure.

Along the lines of right-sizing AI workloads, local computing will play an important role in prototyping, developing, fine-tuning and inferencing GenAI models. Running complex AI workloads locally on AI-enabled PCs has sustainability advantages, as well as other benefits, including cost-effectiveness, improved security and reduced latency.

Optimize data centre performance and energy consumption by returning or recycling technology. Many programmes harvest components and materials to be reused, refurbished and recycled, which reduces e-waste and keeps recycled materials in use longer. Likewise, end-of-life PCs, monitors and accessories can be returned for refurbishment or recycling to keep materials in the circular economy for longer, reducing the demand for new materials.

Within data centre operations, use AI to track and analyze data to improve monitoring and workload placement. This can help optimize efficiency, right-size workloads and reduce energy costs.

Data centre energy use, emissions and e-waste are serious issues the industry is addressing head-on. When approached mindfully, AI infrastructure development can provide a path to more sustainable operations. Recognizing that technology plays an important role in addressing environmental challenges will help our industry collectively harness the tremendous potential for AI to support climate-related solutions. We should all work towards modernizing technology and modelling the 'both/and' benefits of sustainability and AI.

*Gartner, “2023 CEO Survey: Grow Through Digitally Enabled Sustainability,” Kristin Moyer, Mark Raskino, May 19, 2023 (GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.)

^**IDC: Which Circularity Criteria Are Driving IT Planning and Procurement? Doc #US50683123, May 2023