AI RESEARCH
EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization
arXiv CS.LG
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ArXi:2605.14249v1 Announce Type: new We present EnergyLens, an end-to-end framework for energy-aware large language model (LLM) inference optimization. As LLMs scale, predicting and reducing their energy footprint has become critical for sustainability and datacenter operations, yet existing approaches either require production-level code and expensive profiling or fail to accurately capture multi-GPU energy behavior.