{"ID":2826500,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.08844","arxiv_id":"2601.08844","title":"Emissions and Performance Trade-off Between Small and Large Language Models","abstract":"The advent of Large Language Models (LLMs) has raised concerns about their enormous carbon footprint, starting with energy-intensive training and continuing through repeated inference. This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined tasks. Here, we present a comparative analysis of the performance-emissions trade-off between LLMs and fine-tuned SLMs across selected tasks under Natural Language Processing, Reasoning and Programming. Our results show that in four out of the six selected tasks, SLMs maintained comparable performances for a significant reduction in carbon emissions during inference. Our findings demonstrate the viability of smaller models in mitigating the environmental impact of resource-heavy LLMs, thus advancing towards sustainable, green AI.","short_abstract":"The advent of Large Language Models (LLMs) has raised concerns about their enormous carbon footprint, starting with energy-intensive training and continuing through repeated inference. This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined task...","url_abs":"https://arxiv.org/abs/2601.08844","url_pdf":"https://arxiv.org/pdf/2601.08844v1","authors":"[\"Anandita Garg\",\"Uma Gaba\",\"Deepan Muthirayan\",\"Anish Roy Chowdhury\"]","published":"2025-12-21T07:00:22Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.CY\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}