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Description
This white paper explores the evolving landscape of edge AI, examining the drivers of adoption and the shifting mix of AI workloads. It highlights the importance of a systems-level approach to integration, the role of open software and standards in supporting vendor flexibility, and total cost of ownership considerations that can impact deployment decisions. The discussion addresses how energy efficiency, interoperability, and the rise of open source foundational models are accelerating the democratization of AI for the edge. The paper provides a future outlook for edge AI adoption, outlining key challenges and offering recommendations for successful edge AI deployments.