What is it about?

The high energy consumption of artificial intelligence poses a significant environmental challenge. However, a significant 'hidden' carbon cost is associated with manufacturing the powerful GPUs that AI relies on. Our research identifies this hidden cost as a crucial oversight in the pursuit of sustainable AI. This paper presents a practical framework for chip designers to incorporate manufacturing carbon costs directly into their existing, optimized workflows. Rather than requiring drastic changes, we show how to align carbon cost considerations with their current objectives - performance, power, and size. By integrating this carbon cost into design software, we provide a much-needed bridge between sustainability and practicality, paving the way for the development of genuinely environmentally friendly AI hardware.

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Why is it important?

This work is timely, addressing the urgent need to mitigate the environmental impact of AI hardware manufacturing as demand increases. Our research focuses on a critical question: how can we create greener AI chips without stifling innovation? What makes our approach unique is its focus on practicality. We provide a blueprint for chip designers to integrate their manufacturing carbon footprints into their workflows with minimal disruption. This highlights that sustainability and high-performance design can coexist. Our methods provide an essential bridge between environmental responsibility and industrial needs.

Perspectives

As a computer scientist passionate about sustainability, I have observed a gap between 'green' concepts and their practical application in the fast-paced tech industry. In this project, I aim not only to highlight the hidden environmental costs of producing AI chips but also to propose a viable solution for engineers. This approach is rooted in the philosophy of 'minimal disruption,' which respects the existing optimizations in chip design. My goal is for this paper to act as a bridge, demonstrating how we can integrate sustainability into advanced technologies - not by compromising efficiency, but by adopting smarter and more holistic design practices. Ultimately, it is about making the best choice, the easiest one.

Wenkai Guan
University of Minnesota Morris

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This page is a summary of: Green GPU: Integrating Carbon Metrics into GPU Manufacturing with Minimal Disruption, ACM SIGMETRICS Performance Evaluation Review, August 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3764944.3764963.
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