BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving
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.
Sources
- LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
- More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU
- The Llama 3 Herd of Models
- SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- gpt-oss-120b & gpt-oss-20b Model Card
- Qwen3 Technical Report
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