Carbon-aware Generative AI

A practical framework for sustainable AI

As Generative AI adoption accelerates, inferencing is becoming a major contributor to energy consumption and carbon emissions. This paper explores how enterprises can improve AI sustainability through carbon-aware scheduling, model optimization and more efficient infrastructure. Learn how organizations can reduce inferencing emissions 30-47% without compromising service quality.

Key takeaways

• Why AI inferencing is emerging as a significant sustainability challenge for enterprises
• How model compression, quantization and architectural optimization can reduce energy consumption
• How the novel CPAS-G framework schedules flexible AI workloads around lower-carbon energy availability while respecting operational requirements
• Ways to improve data center efficiency through power usage effectiveness (PUE) and water-use efficiency (WUE) optimization
• A practical roadmap for building more sustainable AI operations