What Is Local AI? A Plain-English Guide

August 25, 2026 · By Michel Valenzuela, Founder of Daryl

When you ask an AI a question, where does the thinking happen?

It is easy to assume it happens on the device in front of you. You open an application, type something, and receive an answer in the same window.

But the interface and the intelligence can live in different places.

With a cloud AI service, your request travels over the internet to remote computers that process it and send a response back. With local AI, the model generating the response runs on your own device.

Local AI means your computer does the processing.

That distinction changes where your information goes, what you can do without an internet connection, and what you depend on to keep using the software.

What actually runs on your computer?

An AI model is software trained to recognise patterns and perform certain tasks. Depending on the model, those tasks might include generating text, recognising speech, or producing spoken audio.

Running a model locally means loading it onto your computer and using your computer’s processing power and memory to operate it.

For a local text assistant, you enter a question, the model processes it on your machine, and the response appears in the application.

You do not need to understand the engineering behind that process to use it. Just as you can edit a photograph without knowing how an image-processing algorithm works, you should be able to use local AI without becoming an AI engineer.

Making that experience approachable is a substantial part of the work.

Does local AI work without the internet?

It can, once the necessary software and models are installed.

If an application processes your request entirely on your device, it does not need an internet connection to generate that response. That makes offline conversation possible.

However, different features have different requirements.

Downloading a model, installing updates, searching the web, or connecting to an online service requires connectivity. An application can combine local processing with online features, so the word “local” alone does not tell you how every part of it works.

A useful question to ask is:

Which tasks happen on my computer, and which tasks send information elsewhere?

The answer should be clear enough for you to make an informed choice.

Why does that matter for privacy?

Think about what you might discuss with a personal assistant.

An unfinished business idea. A difficult conversation you are preparing for. A decision you have not shared with anyone. A rough draft that reveals more than the final version ever will.

When processing happens locally, the content of that request can stay on your device rather than being transmitted to a remote AI service.

That gives local AI a practical privacy advantage. It reduces the need to share information in the first place.

Still, local processing is one part of privacy. The application’s behaviour matters too: what it stores, whether it sends diagnostics, whether it uses cloud features, and whether other services sync or back up its files.

A well-designed experience should make those boundaries understandable and give you control over the information it keeps.

What are the trade-offs?

Your computer has finite resources.

A model needs memory and processing power, and those requirements vary considerably. The experience depends on the model, the application, the hardware, and the task you give it.

Larger models may offer broader capabilities, but they generally demand more resources. Smaller models can be practical for particular tasks, especially when the application is designed around their strengths.

Cloud services can draw on infrastructure far beyond what a personal computer provides. That can make them a better fit for demanding work or capabilities a local application does not support.

Local AI is useful when its capabilities fit your needs and you value having the processing on your own machine.

It also shares a limitation with other AI: it can make mistakes. Keeping a model on your computer does not make every answer accurate. Your judgment remains essential.

Is a local model the same as a personal assistant?

A model is an ingredient. An assistant is the experience built around it.

An everyday assistant needs an interface people can understand. Voice interaction, when offered, needs to handle listening and speaking comfortably. Stored context needs clear controls. Installation and updates need to be manageable.

Those details determine whether someone can actually use the intelligence available on their computer.

This is where my interest in local AI becomes an interest in product design.

I want people to be able to open an application and begin thinking through something that matters to them. The technology should support that process with as little friction as possible.

Where Daryl fits

Daryl is a personal AI for Mac, currently in development, designed around local conversation and support for decisions.

The idea is straightforward: give people a place to talk or type, explore a question, compare possibilities, and find greater clarity, with core AI processing on their own computer.

Voice is central to the experience I’m building. So is the ability to examine competing priorities and trade-offs while keeping the final decision in your hands.

Local memory and continuity are part of the development roadmap. The aim is to carry useful context forward under your control, so future conversations can begin with a better understanding of what matters to you.

Daryl also follows a purchase model with no mandatory monthly subscription. Local processing and software pricing are separate choices, but both reflect the same intention: give people more control over the tools they use.

A more personal relationship with technology

For me, the appeal of local AI begins with something familiar.

A personal computer is a place where we keep our work, develop ideas, and make things. As those computers become capable of running useful AI, there is an opportunity to bring more of that intelligence into the space we already control.

That is the opportunity I’m building Daryl around.

A place to think out loud. A tool to help you examine a decision. Intelligence close enough to be useful, with boundaries you can understand.

Daryl is coming soon to Mac.