How it works
On-device AI, or on-device AI, is the processing of artificial intelligence tasks directly on a user's equipment, such as a phone, computer or connected device. Instead of sending all input to a remote server, the device runs part or all of the model locally, using CPU, GPU or a specialized accelerator, such as an NPU.
The flow depends on the product. An app can use a model downloaded to the device, an operating system component or an embedded library. The model receives data, calculates an output and the application decides how to display it. Some solutions switch between local and remote processing, for example when the task requires more capacity.
Running on device imposes limits on memory, power, temperature and storage. For that reason, smaller models, quantization techniques and hardware acceleration can be used to adapt inference. The team needs to measure quality and response time on the actual device, not just on a more powerful development machine.
It is also necessary to check network behavior. An experience called “on-device AI” can still query external services for certain functions, download updates or sync data. The app documentation and settings should clarify which operations are local and which depend on the cloud.
Practical example
Imagine a notes app that transcribes a recording on the phone itself. The audio is processed by the model installed on the device and the text appears on screen. The person can correct names and technical terms before saving or sharing the transcription.
The team developing the app tests different recordings, accents and noise conditions on compatible devices. It also evaluates how long the task takes, how much battery it consumes and whether the device heats up. If the model does not fit or does not reach the required quality, the product can offer another option, explaining when processing will be remote.
To protect privacy, the app avoids sending audio to the network when local mode is selected, informs where the transcription is stored and allows it to be deleted. These controls need to be implemented and tested; the mere presence of a model on the device does not guarantee them.
What it is for
On-device AI can reduce dependency on connectivity, respond with low latency and process data without forwarding it to a remote service for certain tasks. It is useful in mobile features, accessibility, sensors and applications that need to keep working offline.
There are trade-offs. A local model can be smaller or run more slowly than a service hosted on dedicated infrastructure. The team also needs to distribute updates, handle different hardware configurations and offer an alternative when the device is not compatible.
The decision should start from the product requirement: which data will be processed, what quality is acceptable, what latency matters and which devices need to be supported. A hybrid architecture can combine local inference for simple tasks with an optional remote service for more complex operations.
Difference between on-device AI and local inference
Local inference is the broader concept of running a model on locally controlled infrastructure. On-device AI emphasizes that processing occurs on the user's device or on equipment close to the product's main function.
Both can reduce certain cloud calls, but they do not ensure privacy by themselves. It is necessary to review the entire app, its connections, permissions, storage and data policies.