AIBearisharXiv – CS AI · 10h ago7/10
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The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference
A comprehensive study reveals that multilingual LLM inference consumes vastly different amounts of energy across languages, with Pashto requiring 179 times more energy than English for identical requests. The disparity stems from complex script processing and token generation inefficiency in low-resource languages, compounded by a double penalty where high-energy languages also deliver lower accuracy.