// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Edge Deployment
Edge deployment involves running AI models directly on devices closer to where the data is generated, like smartphones or IoT devices, instead of sending all data to a central cloud.
TECHNICAL DEFINITION
Edge deployment positions AI models and inference engines directly on local devices (e.g., IoT sensors, mobile phones, embedded systems) at the "edge" of the network, minimizing data transfer latency, enhancing privacy, and enabling offline operation by reducing reliance on centralized cloud infrastructure.
BACKGROUND
Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- On-device AI
- local AI
- distributed AI (at the edge)
USAGE NOTE
Edge deployment is vital for applications requiring low latency, privacy, or operation in environments with limited connectivity.