The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference
Large language models (LLMs) are increasingly deployed in multilingual settings, yet the energy costs of serving these models across different languages remain poorly understood. We present a systematic study of inference energy consumption across languages with ML.Energy framework (Chung et al., 2026). We find striking disparities: energy consumption per output token varies by up to 8.3 times across languages, while total energy for a fixed set of requests varies by up to 179 times between the cheapest (English, 17.6 kJ) and the most expensive (Pashto, 3,147 kJ) languages. Our analysis shows that this disparity is driven by two compounding factors: (1) higher per-token energy costs for l...
| Authors | Naihao Deng, Alissa Shen, Yiming Feng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 June 2026 |
| Last updated at source | 14 September 2026 |
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