Analytic Framework for Estimating Memory Cost
For AI researchers and data center operators, this framework provides a foundational tool to estimate and mitigate the ecological footprint of AI models.
The paper presents a generalized framework to quantify the energy costs of AI models, including LLMs and DNNs, addressing the growing environmental impact of memory consumption in data centers.
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.