Frontier vs local
How do frontier and local models differ on privacy and control?
A frontier model sends every prompt to the vendor's servers, which you have to trust with your data. A local model keeps prompts and outputs on hardware you control, which removes a whole category of exposure but makes you responsible for securing that hardware yourself.
The clearest reason to run a model yourself has nothing to do with capability or cost. It is where your data goes.
With a frontier model, every prompt you send travels to the vendor’s servers to be processed. That means you are trusting another company with whatever is in those prompts, which can be a real problem for sensitive material such as health records, legal files, or private business data. With a local model, one analysis puts it plainly: your prompts and outputs never leave your own infrastructure, and no third-party provider sees them. That removes a whole category of compliance risk.[1]
Running the model yourself also means you control it. The weights sit on your hardware, so the model cannot be changed or retired out from under you, and you can fine-tune it for your own work.[2]
This control comes with a duty. Keeping data on your machine does not make security automatic; the same analysis notes you are now the one responsible for protecting that hardware and the data on it.[1] The exposure does not vanish, it moves to you.
A handful of free tools make running a model locally a manageable task rather than a research project.
Tools for running models on your own machine
- Ollama ↗
One-command tool to download and run open models locally, with a built-in local API.
- llama.cpp ↗
The open-source engine many local tools are built on; runs models on ordinary CPU or GPU hardware.
- Hugging Face ↗
The main hub for downloading open-weight model files in the first place.