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MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI

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arxiv 2410.12032 v2 pith:RIIE3BY3 submitted 2024-10-15 cs.AR cs.DCcs.LG

Arya Tschand , Arun Tejusve Raghunath Rajan , Sachin Idgunji , Anirban Ghosh , Jeremy Holleman , Csaba Kiraly , Pawan Ambalkar , Ritika Borkar
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Ramesh Chukka Trevor Cockrell Oliver Curtis Grigori Fursin Miro Hodak Hiwot Kassa Anton Lokhmotov Dejan Miskovic Yuechao Pan Manu Prasad Manmathan Liz Raymond Tom St. John Arjun Suresh Rowan Taubitz Sean Zhan Scott Wasson David Kanter Vijay Janapa Reddi
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classification cs.ARcs.DCcs.LG
keywords systemsefficiencyenergypoweracrossbenchmarkingmlperfdevices
verification ladder T0 review T1 audit T2 compute T3 formal
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Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EcoServe: Designing Carbon-Aware AI Inference Systems

    cs.DC 2025-02 conditional novelty 6.0 of 10

    EcoServe combines four strategies (reuse, rightsize, reduce, recycle) in an ILP optimizer to cut modeled carbon emissions for LLM serving by up to 47% while keeping SLOs.

  2. From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

    cs.LG 2026-07 reject novelty 4.0 of 10

    An analytical, calibration-based estimator of GPU inference energy for LLMs splits energy into compute, parameter access, KV-cache writes, and attention reads, but validates against the same data it was fitted on.

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