The Real Problem: On-Device AI Fragmentation and Bottlenecks For years, the promise of "On-Device AI" has been hampered by a frustrating paradox. We have increasingly powerful hardware — specialized NPUs and multi-core GPUs on our phones and edge devices — yet the software stack to utilize them has remained fragmented and often inefficient. Developers building mobile or edge applications faced three brutal pain points: Framework Lock-in: If you trained a model in PyTorch or JAX, the road to high-performance on-device deployment was paved with manual, error-prone conversions. "Translate this model to TFLite" often meant losing performance or, worse, completely breaking the model architecture. The Silicon Gap: TFLite was revolutionary, but it struggled to keep pace with the explosion of custom Neural Processing Units (NPUs) coming from vendors like Qualcomm, MediaTek, and Apple.…