A Google DeepMind blog post outlines a “Gemini computer-use model” which distributes AI computation intelligently between local devices and cloud servers. The goal is to reduce latency, preserve privacy, and optimize bandwidth by processing certain tasks on device (e.g. quick responses, sensitive data) and offloading heavier workloads to cloud.
The article discusses architecture choices, resource constraints, and how the model adapts dynamically to device capabilities, network conditions, and energy usage.
Google claims this paradigm enables more responsive, resilient AI experiences across devices, while maintaining safety and control over critical computation flows.
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