Addressing the Environmental Footprint while Optimizing the Handprint of AI

Artificial Intelligence (AI) and generative AI (GenAI) offer a transformational promise to propel essentially all industries and the global economy forward. Data is essential to the learning and decision-making power 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 compute power, making data center 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 rely on energy and resources, which 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 are slowing down.

In May 2023, Gartner® stated in its report “most CEOs (94%) will increase or hold sustainability and ESG investments at similar levels to 2022.” 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.

These findings appear to be at odds with one another – companies are simultaneously increasing investment in sustainability and in an energy-intensive technology. But sustainability and AI does not have to be “either/or” decision. In fact, 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.

Navigating “Both/And”

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 will be 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 center energy consumption is increasingly becoming a top priority. According to IDC, the number one sustainability priority for IT planning and procurement among IT decision-makers is reducing data center energy consumption. Practical solutions can help make this priority a reality:

• Use energy-efficient, sustainable technology. Minimize AI’s carbon footprint through modern, energy-efficient servers and storage devices and environmentally responsible cooling methods, while powering data centers with renewable energy. Use PCs and other hardware that deliver energy efficiency and include sustainable materials like recycled, ocean-bound or bio-based plastics, low-carbon emissions aluminum, closed-loop materials, and recycled packaging.
• Right-size AI workloads and data center economics. 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 center efficiency. And, flexible “pay as you go” spending models can help organizations save on data center costs while supporting sustainable IT infrastructure.
• Recognize the power of local computing. 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.
• Responsibly retire inefficient hardware. Optimize data center performance and energy consumption by returning or recycling technology. Many programs 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 need to develop new materials.
• Apply AI to find efficiencies. Within data center 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.

Leading by Example

Data center 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 has 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 modeling the “both/and” benefits of sustainability and AI.

Source: dell.com