HOW IT WORKS

This is how local AI works on your own computer

With a tool like Ollama, an open AI model can be run on your own computer instead of in an external cloud service. It gives greater control over the data flow, but places demands on model selection, memory and secure configuration.

Publicerad 21 July 2026, 20.04

AI-generated illustration of a laptop where an AI model and a document stay within a clear local boundary while a cloud symbol is disconnected.

Local AI means that an AI model runs on your own computer or on a server that the business itself controls. Ollama is a tool that downloads and runs open models on macOS, Windows and Linux. When a model is run locally, the question and answer itself do not need to be sent to an external model service.

First, a model file is downloaded to the computer. Then Ollama starts the model and makes it available for a chat or another program. Ollama's local programming interface normally uses the address localhost, i.e. the same computer, and can show which models are saved and which are running.

The computer's memory and the size of the model determine what works in practice. A smaller model normally requires less storage and working memory but may also be weaker on certain tasks. The name of the model is not enough to judge the quality. Version, quantization, license and how the model was tested also matter.

Local is not the same as fully offline or automatically secure. Ollama also has cloud capabilities, and other programs can pass data to web search, analytics tools, or external APIs. Ollama's documentation describes a special mode where the cloud functions can be turned off.

A local model can still give incorrect responses, read files it has accessed, or be affected by insecure extensions. Whoever handles sensitive material therefore needs to control the entire chain: model file, app, network connections, logs, permissions and where the result is saved.

Därför spelar det roll

Local driving can give Swedish companies, schools and private individuals greater control over work materials and reduce dependence on a network connection. It can also make the cost more predictable for recurring tasks. The profit only occurs when the right model fits on the hardware and the entire workflow is actually kept local.

Det här kan du göra

  1. Start with material that is not sensitive and check what network calls the program makes.
  2. Choose a model that fits in the computer's memory and read the model card, license and version information before use.
  3. Compare the answers with a known task and measure quality, speed and resource usage on your own computer.