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ML-EcoLyzer: Quantifying the Environmental Cost of Machine Learning Inference Across Frameworks and Hardware

arXiv 2511.06694 ↗First posted 10 Nov 2025cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-framework tool for measuring the carbon, energy, thermal, and water costs of inference across CPUs, consumer GPUs, and datacenter accelerators. The tool supports both classical and modern models, applying adaptive monitoring and hardware-aware evaluation. We introduce the Environmental Sustainability Score (ESS), which quantifies the number of effective parameters served per gram of CO2 emitted. Our evaluation covers over 1,900 inference configurations, spanning diverse model architectures, task modalities (text, vision, audio, tabular), hardware types, and precision levels. These rigorous and reliable measurements demonstrate that quantization enhances ESS, huge accelerators can be inefficient for lightweight applications, and even small models may incur significant costs when implemented suboptimally. ML-EcoLyzer sets a standard for sustainability-conscious model selection and offers an extensive empirical evaluation of environmental costs during inference.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 10 Nov 2025.

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References · 10

  1. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 2,031
  2. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  3. 2017Mixed Precision Trainingcited by 1,156
  4. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  5. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  6. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
  7. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 184
  8. 2019MLPerf Training Benchmarkcited by 115
  9. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
  10. 2025Holistically Evaluating the Environmental Impact of Creating Language Modelscited by 26

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  6. 2026Watt Counts: Energy-Aware Benchmark for Sustainable LLM Inference on Heterogeneous GPU Architectures
  7. 2025Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights
  8. 2025Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency