Edge Computing

Infrastructure
About 1 min read

A distributed computing paradigm that processes data near where it is generated (the edge) to improve response times and save bandwidth costs.

Also known as
Edge ComputingEdge

Detailed explanation

Edge computing is a method of processing data directly where it is generated—such as on smartphones, IoT sensors, and autonomous vehicles—rather than relying on centralized data centers or the cloud. In the AI field, this extends to 'on-device AI' or 'edge AI.' It minimizes latency to enable real-time inference, and protects privacy since sensitive data does not need to be transmitted externally. Recently, with the proliferation of NPUs (neural processing units) and advancements in model compression techniques, it has evolved to run high-performance large language models (LLMs) locally on devices.

Why it matters for tool selection

Support for edge computing is a key metric when choosing AI tools for services requiring real-time response (such as autonomous driving or manufacturing process monitoring) or where privacy is the top priority (healthcare/finance). It is essential for business models that need to reduce cloud costs while guaranteeing stable operation in offline environments.

What to check

  • Optimization support for hardware accelerators (NPUs, GPUs) on the target devices
  • Acceptable tolerance for accuracy loss during model compression (quantization)
  • Stability of local operation in the absence of internet connectivity
  • Data synchronization and management policies between edge nodes and the centralized cloud

Example

Autonomous cars must make decisions within milliseconds to identify surrounding objects. Performing inference immediately on a dedicated in-vehicle AI chip, rather than sending data to the cloud and waiting for a response, is a classic example of edge computing.

Commonly confused terms

Cloud Computing

Processing complex training or large-scale computations using massive resources in centralized servers.

On-Device AI

A form of edge computing focused specifically on running AI models directly on user terminals (phones, PCs, etc.).

Related terms

On-device AINPUCloudLatencyQuantization