AI RESEARCH
ML-EcoLyzer: Quantifying the Environmental Cost of Machine Learning Inference Across Frameworks and Hardware
arXiv CS.CV
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ArXi:2511.06694v1 Announce Type: cross 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 s both classical and modern models, applying adaptive monitoring and hardware-aware evaluation.