The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices.Moving AI on-device is a brilliant and necessary strategy. It ensures absolute user privacy in alignment with EU regulations, fundamentally shifts the economics away from costly cloud inference, and paves the way for a significant hardware upgrade supercycle as users seek true AI-capable silicon.To create a smart on-device "Semantic Router," models need to reach the 27B+ parameter scale. Achieving this on a phone requires extreme quantization, such as PrismML’s ternary weights.However, a critical hardware reality often overlooked by the software world is that fitting the weights in RAM is not equivalent to moving them. Running a 27B ternary model on standard LPDDR encounters a significant memory bandwidth limitation. Transferring gigabytes of data across the SoC bus for each token generation can lead to thermal...