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arXiv · 2608.19994

Green BOA: Determining the environmental break-even point for ML-based data compression

Caterina Doglioni·Thomas Elliott·Akshat Gupta·Hanzila Hussain·Sanjiban Sengupta·Zhengkai Sun
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Abstract

We summarise the outcome of two summer internship projects based at the University of Manchester, focused on the break-even point in terms of environmental sustainability for ML-based data compression algorithms. Using the example of a ML-based lossless compression algorithm, we compare estimates for the carbon-equivalent of the infrastructure needed for ML training and inference with the carbon-equivalent savings from reduced disk storage requirements, and discuss their break-even point.

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Caterina Doglioni, Thomas Elliott, Akshat Gupta, Hanzila Hussain, Sanjiban Sengupta, Zhengkai Sun. 2026-08-20. Green BOA: Determining the environmental break-even point for ML-based data compression. https://arxiv.org/abs/2608.19994

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