Infrastructure & Deployment

Edge AI

Running AI models directly on local devices rather than in the cloud.

Definition

Edge AI runs AI models on the device where data is generated — a smartphone, a factory sensor, a retail terminal — rather than sending data to a cloud server for processing. This enables real-time AI that works offline, has no network latency, and doesn't require sending potentially sensitive data to external servers. Advances in model compression and custom chips are making increasingly capable AI viable on edge devices.

Why this matters for your business

Edge AI is particularly valuable for industrial, retail, and healthcare applications where connectivity is unreliable, data privacy is paramount, or latency requirements are too tight for cloud round-trips.

Related Terms

Cloud AI

AI models and services accessed over the internet via a provider's infrastructure.

On-Premises AI

Running AI models on hardware you own and control, within your own environment.

Quantisation

Reducing a model's numerical precision to make it smaller and faster, with minimal quality loss.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Infrastructure & Deployment·Edge AI