Analytic Framework for Estimating Memory Cost
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914527227936768 |
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| author | Shankar, Anirudh Chatterjee, Avhishek Chakravorty, Anjan |
| author_facet | Shankar, Anirudh Chatterjee, Avhishek Chakravorty, Anjan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01793 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Analytic Framework for Estimating Memory Cost Shankar, Anirudh Chatterjee, Avhishek Chakravorty, Anjan Emerging Technologies Applied Physics 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. |
| title | Analytic Framework for Estimating Memory Cost |
| topic | Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2605.01793 |