Run powerful open models on your own machine — offline, private, no subscription, no data leaving home.
Every business runs on information it would never post in public: client lists, draft contracts, salary bands, the half-formed pricing model you are still testing, the email where a customer explained why they almost left.
Picture the moment that tests whether your setup lasts: eight months from now a new open model lands that everyone online calls a leap. You already have a model that launches in one click, a default you chose on purpose, and a maintenance rhythm on your calendar — now you find out whether it holds.
Open any forum about running AI locally and the same panic repeats: someone wants to try a private model, reads that "real" AI needs a $1,600 graphics card, and either overspends on hardware they will barely tax or quits before installing anything.
You get a model that answers with the Wi-Fi off. It is a generalist that will cheerfully attempt anything — and attempt some things badly.
Installing Ollama or LM Studio and turning off your Wi-Fi feels like privacy. But feeling private and being private are not the same thing.
Most local AI setups die quietly. You install Ollama on a Saturday, run a few prompts that genuinely impress you, mention it to a colleague on Monday — and a month later the icon sits untouched in your dock.
Work through it for your own situation and write your answers — a workbook only works if you fill it in. Score each statement from 0 (not at all true) to 3 (very true), starting with: a meaningful share of my AI tasks involve confidential client, employee, or financial data.
You are ready to move into the mechanics once you can check these.
Take a concrete situation instead of a hypothetical. Say you run operations for a small professional-services firm: walk through a week of AI-assisted tasks and sort each one. Summarizing a 40-page client contract before a renewal is local — the document is confidential and the task is mechanical.
Pick three candidate models from any open-model listing, such as the Ollama library or a repository that offers GGUF downloads. For each, fill in the line and decide go or no-go for your machine.
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