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• A reproducible experimental pipeline was constructed to profile the interplay between energy consumption, latency, and quality of on-device LLMs.method 95%
• Modern importance-aware quantization yields negligible energy savings compared to standard mixed-precision methods for LLMs on mobile devices.result 90%
• For battery life on mobile devices, the architecture of an LLM is a more decisive factor than its quantization scheme.result 90%
• Mixture-of-Experts (MoE) architectures offer the storage capacity of a 7B model while maintaining the lower energy profile of a 1B to 2B model.result 95%
• Mid-sized LLMs, such as Qwen2.5-3B, represent a pragmatic sweet spot balancing response quality with sustainable energy consumption on-device.result 90%
• The experimental pipeline captured granular power metrics across eight LLMs ranging from 0.5B to 9B parameters without requiring root access.method 95%
• The study utilized the llama.cpp inference engine for executing LLMs on the Android device.method 90%

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