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Arxiv
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Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations
- This study presents an empirical investigation into the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines.
- For Discriminative models, the study examines various architectures and hyperparameters during training and inference and identifies energy-efficient practices.
- For Generative AI, the study focuses on Large Language Models (LLMs) and assesses energy consumption across different model sizes and varying service requests.
- The results indicate that optimizing architectures, hyperparameters, and hardware can significantly reduce energy consumption for Discriminative models without sacrificing performance.
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