BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866917542108332032 |
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| author | Shi, Tianyao Ding, Yi |
| author_facet | Shi, Tianyao Ding, Yi |
| contents | 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 actionable quality-aware serving tradeoffs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27480 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving Shi, Tianyao Ding, Yi Other Quantitative Biology Artificial Intelligence Computers and Society 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 actionable quality-aware serving tradeoffs. |
| title | BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving |
| topic | Other Quantitative Biology Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2605.27480 |