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

arXiv:2605.27480 · q-bio.OT, cs.AI, cs.CY · Submitted 2026-05-26 · Read on arXiv

q-bio.OT, cs.AI, cs.CY

Submitted: 2026-05-26

Updated: 2026-08-29

Comments: 23 pages, 27 figures, 10 tables, the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP), Budapest, Hungary, October 24-29th, 2026

Journal ref: Findings of the Association for Computational Linguistics: EMNLP 2026

License: http://creativecommons.org/licenses/by/4.0/

The gist: Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways.

Terminology

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, BIRDS reveals that biodiversity impact accumulates at scale and exposes quality-aware serving tradeoffs. The code is available at https://github.com/TianyaoShi/BIRDS.

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