Can a local AI translate documents?
Yes. Translation is a core language task, not a cloud feature, and the same model that reads your sources can carry them across languages.
Yes. Translation is a core language task, and a model that runs on your machine handles it the same way it handles summarizing or rewriting. Quality follows model size and the language pair, and a local model can be swapped for a stronger one without changing anything else. Documents stay on your disk while they are being worked on, so confidential material never passes through a translation service to become readable.
Translation is a language task, not a service.
The cloud translators people reach for are doing the same job a local model does: reading text in one language and producing text in another. Nothing about that job needs a server. A model that generates answers from your sources also generates the same passage in another language, and it does so with the context of the whole document in view, not just the pasted paragraph. That is why a local model is a reasonable translation tool and not a compromise: the machinery is identical, only the location differs.
Where quality actually comes from.
Translation quality tracks model size first and language pair second. High-resource languages, the ones with oceans of published text, come through well. Low-resource pairs are where a small local model shows its limits, and the failure is usually soft: stilted phrasing and dropped nuance rather than wrong meaning. The honest test costs nothing. Run a paragraph you already understand through the model and read what comes back the other way. If meaning survives the round trip, trust it on that pair; if it does not, that is a finding about the model, not about local translation.
What the document never has to do.
The standard pattern for translating a confidential document is to paste it into a web form and hope. Once submitted, the text sits on someone else's servers, sometimes retained, sometimes used for improvement you never agreed to. A local loop cuts that step out entirely: the file is read from disk, translated on your hardware, and written back, and the only network traffic is whatever you chose to fetch. For anything under NDA, embargoed, or simply unpublished, that difference is the whole decision. It is the same rule that keeps a local research loop private: work with the text where the text lives.