The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices
The rapid diffusion of generative artificial intelligence raises privacy, latency, and performance concerns that motivate a shift toward "local-first" AI, where inferences are performed on the user's device instead of on remote cloud servers. This paradigm also places a significant computational load on battery-powered smartphones, potentially shortening battery life and increasing the overall replacement rate of mobile devices. This paper presents a systematic study of the energy consumption, performance, and accuracy of on-device large language model (LLM) inference. We evaluate 18 models from different model families, sizes, and quantization levels, on two modern smartphones and on a s...
| Authors | Édouard Guégain, Tristan Coignion |
|---|---|
| Primary category | cs.PF |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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