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 has led to significant advances in natural language processing and text generation. However%2C the energy consumed through LLM model inference remains a major challenge for sustainable AI deployment. To address this problem%2C we model the workload-dependent energy consumption and runtime of LLM inference tasks on heterogeneous GPU-CPU systems. By conducting an extensive characterization study of several state-of-the-art LLMs and analyzing their energy and runtime behavior across different magnitudes of input prompts and output text%2C we develop accurate (R^2>0.96) energy and runtime models for each LLM. We employ these models to explore an offline%2C energy-optimal LLM workload scheduling framework. Through a case study%2C we demonstrate the advantages of energy and accuracy aware scheduling compared to existing best practices.?quality=80&w=800)
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