This white paper explores the evolution of generative AI from massive data center models to efficient, scalable deployment at the edge. It highlights the emergence of smaller LLMs (5–50B parameters) and open-source innovations enabling real-time, privacy-preserving inference on devices like cameras, robots, and PCs. Key challenges include power, latency, and cost, requiring custom SoCs like Ambarella’s N1, which runs multimodal models efficiently under 50W. The future lies in specialized, fine-tuned AI tailored to user data, supported by edge-friendly hardware with unified memory and developer-first ecosystems.

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