A new research paper published on arXiv explores the computational and energy costs associated with Large Language Models (LLMs), particularly within agentic architectures that utilize extensive input contexts. The study reveals that while decode compute time and energy scale linearly with output tokens, prefill compute time and energy show both linear and quadratic scaling with input tokens. This indicates that input context significantly impacts resource consumption for large-context workloads. The research proposes a framework using compute time as a proxy for energy consumption, enabling more accurate estimations and promoting sustainable deployment of AI. AI
IMPACT Provides a framework for estimating LLM energy consumption, aiding in sustainable AI deployment decisions.
RANK_REASON Academic paper detailing computational and energy costs of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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