Emissions and Performance Trade-off Between Small and Large Language Models

• URL: http://arxiv.org/abs/2601.08844v1
• Date: Sun, 21 Dec 2025 07:00:22 GMT
• Title: Emissions and Performance Trade-off Between Small and Large Language Models
• Authors: Anandita Garg, Uma Gaba, Deepan Muthirayan, Anish Roy Chowdhury,
• Abstract summary: This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined tasks.<n>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.
• Score: 1.0863226323853896
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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