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Edge AI

Edge AI is running AI models directly on devices, smartphones, laptops, cameras, industrial machinery, vehicles, instead of remote cloud servers. "Edge" means the network's edge: processing happens where the data is born.

Definition

Advantages: minimal latency (no round trip to the cloud), offline operation, structural privacy (data never leaves the device) and lower operating costs at high volume. Limits: local compute power constrains model size, this is the natural territory of SLMs and quantized models.

It's the direction much of the industry is heading: phones run local language models for summaries and writing, PCs integrate neural accelerators (NPUs), industry uses on-device computer vision for real-time quality control. The emerging pattern is hybrid: edge for fast, sensitive tasks, cloud for heavy ones.

Related terms

• SLM (Small Language Model)
• Latency
• Privacy by Design
• Inference
• Computer Vision

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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