Silicon Ready, Stack Incomplete: The Real Challenge of On-Device AI

With NVIDIA's RTX Spark and Apple's M-series chips, the hardware bottleneck for on-device AI inference has been cleared, enabling consumer devices to run large language models efficiently. However, substantial gaps persist between having capable hardware and deploying functional AI agents, requiring solutions in inference optimization, quantization techniques, and higher-level abstraction layers. This analysis reveals that while chip manufacturers have delivered their piece, the software engineering challenge remains the critical bottleneck for real-world on-device AI applications.