What does it mean to run AI locally?

Running AI locally means the model does its work on a device you control, such as a laptop, desktop, or a server you operate. Your prompt does not need to be sent to a hosted model service for that particular task. It is one way to build an AI workflow, with a different set of tradeoffs from a cloud service.

Why consider it?

Local AI can be useful when you want more control over where data is processed, when an internet connection is unreliable, or when you want to experiment without paying for every request. It can also make a small, focused tool easier to customize.

Local does not automatically mean private or secure. The app around the model may still send telemetry, download updates, or connect to other services. You still need to understand how the whole workflow handles data.

What changes when you run a model yourself?

You take responsibility for the hardware, setup, updates, and testing. A model that fits on one machine may be slower or less capable than a larger hosted model. The right comparison is the task you need to complete, not the size of the model alone.

Start with a small, repeatable task. Write down a few representative examples, try a local model, and check its answers against your own standard for a useful result. That gives you a clearer basis for deciding whether local AI belongs in the workflow.

A useful first question

Ask: what information does this task use, and where is it allowed to go? The answer helps narrow the options before you spend time choosing a model or tool.

This is the first note in a growing series on local AI. Future guides can get more specific about setup, hardware, evaluation, and practical use cases.