Wasted large language models: A life cycle thinking approach
Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Efforts to increase the energy efficiency of these models have not translated into reduced consumption due to rebound effects such as Jevons Paradox - that increased efficiency drives increased use. There is therefore a need for additional measures to solve this problem. We suggest that one possible way forward is to use life cycle thinking, and view LLMs as products that can become waste. With this perspective, we investigate the potential of the waste hierarchy from the EU's Waste Framework Directive, which suggests five different measures for...
| Authors | Erik Johannes Husom, Maria Emine Nylund, Ophelia Prillard |
|---|---|
| Primary category | cs.CY |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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