Individual GPUs consume up to 1,000 watts to process the massive data loads required by Large Language Models. This power draw equals a household vacuum cleaner, while modern smartphones operate under 1 watt. This extreme disparity creates a critical efficiency bottleneck. Hardware engineers must now bridge this gap to enable sustainable, on-device AI inference.