Edge AI vs. Cloud AI: Architectural Trade-offs

Decentralizing Inference

Historically, complex machine learning inference required cloud computing arrays. With the advent of specialized NPUs (Neural Processing Units), inference is moving to the 'edge'.

Metric	Cloud AI	Edge AI
Latency	High (Network Dependent)	Ultra-Low (Local)
Privacy	Low (Data transmission)	High (Data stays local)

While Edge AI provides real-time processing necessary for autonomous vehicles and robotics, it suffers from storage limitations and higher device costs.

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