BIRDS: 大規模言語モデルサービスの生物多様性影響の特徴づけと理解
BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving
LLMサービスの環境影響を炭素や水だけでなく生物多様性の観点から評価するフレームワークBIRDSを提案し、リクエスト単位の影響と品質を考慮した指標QNBIを導入した。
著者: Tianyao Shi, Yi Ding
分類: q-bio.OT, cs.AI, cs.CY
原文アブストラクト
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.