@inproceedings{fernandez-etal-2025-energy,
    title = "Energy Considerations of Large Language Model Inference and Efficiency Optimizations",
    author = "Fernandez, Jared  and
      Na, Clara  and
      Tiwari, Vashisth  and
      Bisk, Yonatan  and
      Luccioni, Sasha  and
      Strubell, Emma",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1563/",
    pages = "32556--32569",
    ISBN = "979-8-89176-251-0",
    abstract = "As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise. Prior benchmarking efforts have primarily focused on latency reduction in idealized settings, often overlooking the diverse real-world inference workloads that shape energy use. In this work, we systematically analyze the energy implications of common inference efficiency optimizations across diverse Natural Language Processing (NLP) and generative Artificial Intelligence (AI) workloads, including conversational AI and code generation. We introduce a modeling approach that approximates real-world LLM workflows through a binning strategy for input-output token distributions and batch size variations. Our empirical analysis spans software frameworks, decoding strategies, GPU architectures, online and offline serving settings, and model parallelism configurations. We show that the effectiveness of inference optimizations is *highly sensitive to workload geometry, software stack, and hardware accelerators*, demonstrating that naive energy estimates based on FLOPs or theoretical GPU utilization significantly underestimate real-world energy consumption.Our findings reveal that the proper application of relevant inference efficiency optimizations can reduce total energy use by up to **73{\%}** from unoptimized baselines. These insights provide a foundation for sustainable LLM deployment and inform energy-efficient design strategies for future AI infrastructure."
}