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arXiv · 2605.01793

Analytic Framework for Estimating Memory Cost

Anirudh Shankar·Avhishek Chatterjee·Anjan Chakravorty
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Abstract

As artificial intelligence (AI) models quickly spread and become more advanced, they are requiring an ever-increasing amount of data and compute capability, leading to a significant energy cost. Training and inference of AI models including the large language models (LLMs) and deep neural networks (DNNs) are contributing to a large carbon footprint owing to the massive amount of memory they consume in data centers. In this article, we present a generalized framework that quantifies these energy costs incurred to the environment. This framework provides a foundational quantification of AI's ecological footprint, facilitating the development of sustainable architectural strategies for future models.

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Anirudh Shankar, Avhishek Chatterjee, Anjan Chakravorty. 2026-05-03. Analytic Framework for Estimating Memory Cost. https://arxiv.org/abs/2605.01793

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