Smaller models and accelerated compute are transforming AI at the edge.
Artificial Intelligence (AI) is undergoing a fundamental transformation. While early AI models were large, compute-heavy, and dependent on cloud processing, a new wave of efficiency-driven innovations is moving AI inference—the generation of model results—to the edge. Smaller models, improved memory and compute performance, and the need for privacy, low latency, and energy efficiency are driving AI adoption in mobile devices, wearables, robotics, and automotive applications.
However, this shift does not mean that demand for AI compute will decrease. Jevon’s Paradox, the economics principle, tells us that when technological advancements improve efficiency, overall consumption increases rather than declines. The same is true for AI: as models become more efficient, AI adoption will become the norm across industries, with more intelligence embedded into billions of devices and systems worldwide to capture and analyze data.
This Executive Insights briefing paper explores how AI efficiency improvements—driven by model distillation, hardware acceleration, and emerging architectures—are fueling the rapid expansion of AI. We also analyze the impact of recent breakthroughs such as DeepSeek’s ultra-efficient AI models and discuss the critical role of CPUs and accelerator compute subsystems in scaling AI inference at the edge.
Read more here.
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