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Benchmarking LLM Inference Footprint by palannas is a document available to read on EtoBox.

This paper presents a benchmarking framework to assess the environmental impact of large language model (LLM) inference, analyzing 30 models deployed in commercial data centers. The study reveals significant resource consumption, with some models being over 70 times more energy-intensive than others, and highlights the need for standardized methods to evaluate sustainability in AI. Findings indicate that while AI becomes cheaper and faster, its widespread use leads to increased energy, water, and carbon foo

Author

palannas

Language

EN