Powering sustainable AI

Run AI on smarter infrastructure. Breakthroughs are underway with compute-in-memory (CIM) and processing-in-memory (PIM) technologies that integrate hardware and software more efficiently, and neuromorphic computing that mimics the human brain to process data more efficiently.

Use lightweight models and open ecosystems. Lightweight AI models and lower-precision computing formats like Floating Point 8-bit (FP8) reduce memory requirements and allow faster computation, so they use less energy for training and deploying large AI models.

Deploy AI at the edge. Edge AI applications improve performance and cut emissions by processing data locally on devices—great for industries that rely on real-time data processing, such as manufacturing, healthcare, retail and financial services.

Adopt dynamic scaling and smart load balancing. By matching energy use to AI workloads, companies like Meta and Google are reducing power consumption and emissions while meeting ever-increasing demand for AI tools.

Monetize data centers. AI compute marketplaces and GPU-sharing platforms allow individuals and businesses to monetize idle computing resources by transforming them into valuable assets for AI and other applications.

Optimize data center location. Companies are scheduling AI workloads for times and locations where cleaner energy is available and are even exploring ways to harness solar power and natural cooling on the lunar surface.

Integrate low-carbon energy options. Small Modular Reactors (SMRs) present a reliable pathway to consistent power supplies while reducing dependence on fossil fuels. Recent partnerships highlight the growing momentum toward nuclear-powered AI infrastructure.

Use water-wise cooling innovations. Direct-to-chip liquid cooling, evaporative-free cooling and heat reuse systems can minimize water and power consumption. Closed-loop cooling systems and wastewater recycling can potentially produce water savings of 50 to 70%.

Choose AI models of the right size. Platforms like Accenture Model Switchboard optimize AI deployment by selecting cost-effective models for tasks. Retrieval-Augmented Generation (RAG) improves efficiency by accessing data only when needed, while hybrid AI-rule-based approaches can further improve performance.

Incentivize efficiency. Transition from flat-rate AI pricing to usage-based or efficiency-driven pricing models to optimize how and when AI resources are consumed.

Go net-negative. Use AI to minimize AI impacts. AI-driven HVAC systems can slash energy use, predictive logistics can cut transportation emissions and AI-powered smart grids can optimize electricity distribution.

Embed AI sustainability in governance. Real-time energy monitoring is now being used alongside traditional KPIs to track AI carbon intensity and energy efficiency—providing a broader data set to support sustainability initiatives.

Automate governance policies. AI-driven automation can help enforce sustainability policies, manage environmental risks and make it easier to select the most sustainable infrastructure for each model deployment.

Help define AI standards. Initiatives are underway to define uniform global standards and shape AI sustainability standards, providing a rare opportunity for companies to influence smart AI design policies that balance innovation and sustainability.