On-device AI

Infrastructure
About 1 min read

A technology that helps improve security and real-time responsiveness by directly running AI models through hardware (NPU) inside user devices such as smartphones and PCs, without going through cloud servers.

Also known as
Local AIEdge AIEdge AI

Detailed explanation

On-device AI is a technology that independently runs AI models on the computing units (NPU/GPU/CPU) inside the terminal without data transmission to or from external servers. Because data is not transmitted outside the device, privacy protection and security are exceptional, and real-time processing without latency is possible even in environments without a network connection. In particular, as the 'AI PC' standard equipped with NPU computing performance of 40 TOPS or higher and 16GB or more of RAM becomes standardized, it has become possible to stably perform complex language models (SLMs) or AI agent functions within the device. This serves as a key technical selection criterion that reduces cloud API call costs for enterprises, secures data sovereignty, and provides users with a personalized, optimal AI experience.

Why It Matters in Tool Selection

In corporate environments where data security is the top priority, sensitive internal information can be processed locally without being transmitted to external servers. Unlike the cloud approach, it does not incur separate API call costs or network subscription fees, resulting in high long-term cost efficiency, and ensures a consistent response speed even in unstable network environments.

What to Check

  • Check if the NPU performance meets at least 40 TOPS (based on Copilot+ PC)
  • Check system RAM capacity (minimum 16GB, recommended 32GB or more) for running local LLMs
  • Compatibility with AI frameworks supporting hardware accelerators (NPU) such as ONNX Runtime
  • Battery efficiency and thermal management performance during large-scale inference tasks

Examples

Typical examples include real-time call translation performed without an internet connection, local document summarization and personalized search in offline environments, and camera-based real-time object identification and background removal.

Related terms

NPUEdge ComputingSLM (Small Language Model)Quantization