Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View

• URL: http://arxiv.org/abs/2502.11256v1
• Date: Sun, 16 Feb 2025 20:20:18 GMT
• Title: Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View
• Authors: Yanran Wu, Inez Hua, Yi Ding,
• Abstract summary: Large language models (LLMs) offer powerful capabilities but come with significant environmental costs, particularly in carbon emissions. We introduce the concept of a functional unit (FU) and develop FUEL, the first FU-based framework for evaluating LLM's environmental impact. Our findings highlight the potential for reducing carbon emissions by optimizing model selection, deployment strategies, and hardware choices.
• Score: 2.5832043241251337
• License:
• Abstract: Large language models (LLMs) offer powerful capabilities but come with significant environmental costs, particularly in carbon emissions. Existing studies benchmark these emissions but lack a standardized basis for comparison across models. To address this, we introduce the concept of a functional unit (FU) and develop FUEL, the first FU-based framework for evaluating LLM serving's environmental impact. Through case studies on model size, quantization, and hardware, we uncover key trade-offs in sustainability. Our findings highlight the potential for reducing carbon emissions by optimizing model selection, deployment strategies, and hardware choices, paving the way for more sustainable AI infrastructure.

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