Not every AI conversation needs to ride off to somebody else’s server. Running a model locally changes where the work happens—and who has to carry the load.
Bring the model home
Ollama can run language models on your own computer. When a model runs locally, your machine performs the processing. Ollama says it cannot see your prompts or responses when you run locally.
That can be useful when you want to experiment with a model on your own terms. But local processing is a specific configuration, not a property you should assume from a product’s name or interface.
Local and cloud are different trails
Ollama also supports cloud-hosted models. Those requests are processed remotely. If local processing is your goal, select a local model and review the configuration rather than assuming every model is on your machine.
The project documents a local-only setting that disables its cloud models and web search. Downloading a model is also different from running it afterward: getting the files onto your computer may require an internet connection even when later inference is local.
Your hardware does the heavy lifting
Models use system memory, GPU memory, or a combination of the two. Your computer’s available resources influence which models make sense and how responsive they feel. Start with the hardware guidance in the official FAQ.
Keeping the processing nearby does not make every answer trustworthy. A local language model can still invent details or misunderstand a question. Verify anything consequential, and choose a model for the task rather than for the biggest number on the label.
Local AI is about where computation happens. Pick a genuinely local model, check the settings, and keep your judgment switched on.
Don't just take our word for it.
Ollama FAQ: local processing, cloud features, and memory ↗Source-based editorial guide. Sources reviewed October 25, 2026. Illustrations are conceptual and do not depict a reviewed product.
