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Microsoft explains that running AI on devices and environments owned by the customer changes the trust model because models, data, and keys operate outside the direct control of the service provider. It proposes four pillars: proving the integrity of the execution environment, verifying the origin of components, enforcing deterministic mediation over model actions, and releasing sensitive assets only after the required evidence and policies have been satisfied.
Sharad Chol of Expedera analyzes how the transition of edge devices from traditional vision networks to LLM and VLM models is shifting the nature of the bottleneck from computational capacity to memory traffic. He outlines the role of packet-based processing in reducing external data transfers and improving model execution inside vehicles and embedded devices.
NVIDIA showcased the capabilities of the Jetson platform for running AI and robotics at the edge, starting with the Jetson Orin Nano Super for early projects and extending to the Jetson AGX Thor for more complex workloads. The company’s post presents practical examples including autonomous driving, voice assistants and multimodal robots.