BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving

2026-05-26

AI Summary

This study addresses the lack of systematic quantification of the environmental impacts of large language model (LLM) services on biodiversity. It proposes BIRDS, a novel framework that integrates biodiversity considerations into LLM evaluation by introducing a request-level functional unit and combining life cycle assessment with biodiversity modeling to quantify both operational and embodied impacts. The framework further introduces the Quality-Normalized Biodiversity Impact (QNBI) metric, which enables a unified assessment of ecological costs relative to service quality. Multi-dimensional experiments demonstrate that biodiversity impacts accumulate significantly at scale, and that QNBI effectively informs optimization strategies that balance high-quality performance with minimized ecological footprint.

Abstract

Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a framework for Biodiversity Impact of Request-Driven LLM Serving. BIRDS defines request-level functional units, quantifies operational and embodied biodiversity impact, and introduces Quality-Normalized Biodiversity Impact (QNBI) to jointly analyze ecological impact and response quality. Across diverse workloads, models, GPUs, and regions, \SYSTEM{} reveals that biodiversity impact accumulates at scale and exposes actionable quality-aware serving tradeoffs.

Problem

• biodiversity impact
• large language model serving
• environmental impact
• ecosystem damage
• request-driven serving

Innovation

• Biodiversity Impact
• LLM Serving
• Quality-Normalized Biodiversity Impact
• Request-Level Functional Unit
• Environmental Tradeoffs