Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights

• URL: http://arxiv.org/abs/2504.06307v1
• Date: Mon, 07 Apr 2025 21:56:59 GMT
• Title: Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights
• Authors: Tahniat Khan, Soroor Motie, Sedef Akinli Kocak, Shaina Raza,
• Abstract summary: The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions.<n>This paper explores the integration of energy-efficient optimization techniques in the deployment of LLMs to address these concerns.
• Score: 2.1249213103048414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions, posing a critical challenge to the sustainability of generative AI technologies. This paper explores the integration of energy-efficient optimization techniques in the deployment of LLMs to address these environmental concerns. We present a case study and framework that demonstrate how strategic quantization and local inference techniques can substantially lower the carbon footprints of LLMs without compromising their operational effectiveness. Experimental results reveal that these methods can reduce energy consumption and carbon emissions by up to 45\% post quantization, making them particularly suitable for resource-constrained environments. The findings provide actionable insights for achieving sustainability in AI while maintaining high levels of accuracy and responsiveness.

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