Running Gemma4 on Apple Neural Engine (rockyshikoku.medium.com)

A recent exploration revealed the potential of Apple's Neural Engine (ANE) for running Google’s lightweight language model, Gemma 4, despite its architectural challenges. While devices like the A19 Pro boast impressive processing power—approximately 19 teraflops—this capability remains underutilized due to ANE's primary focus on convolution operations rather than the matrix multiplication (matmul) that large language models (LLMs) typically require. Furthermore, Gemma 4’s size, especially in uncompressed form, exceeds the available RAM of devices like the iPhone 17 Pro, necessitating innovative strategies for efficient memory usage and processing. The successful implementation of Gemma 4 on the ANE demonstrated a significant advance, achieving a token generation rate of 31 tokens per second after meticulous optimization of CoreML operations to maximize utilization of ANE's capabilities. However, the endeavor highlighted the inherent limitations in quantization; while experiments with different bit-width configurations revealed a sharp drop in model performance, it underscored the delicate balance between resource constraints and computational fidelity. The engagement of the community in overcoming these technical hurdles paves the way for more robust applications of AI on mobile devices, leveraging the ANE's unique architecture for advanced processing tasks.