arXiv:2605.27480v1 Announce Type: cross 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 biodiversi
The rapid expansion of large language models and their infrastructure is making the environmental impacts, beyond carbon and water, increasingly evident and quantifiable.
This introduces a new, previously under-recognized environmental constraint on AI development and deployment, which can influence regulation, resource allocation, and public perception.
The environmental footprint of AI now explicitly includes biodiversity impact, necessitating new frameworks for measurement and mitigation and potentially shifting investment towards more sustainable AI practices.
- · Environmental consulting firms
- · Sustainable AI research initiatives
- · GPU manufacturers with superior efficiency
- · Energy-intensive data centers
- · LLM providers ignoring environmental impact
- · Hardware manufacturers with inefficient designs
Demand will grow for more energy-efficient AI hardware and software architectures.
Regulatory bodies may begin to incorporate biodiversity impact into environmental assessments for large-scale AI infrastructure projects.
Public pressure and ethical considerations could lead to a 'green AI' movement, influencing consumer choice and corporate strategy.
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Read at arXiv cs.AI