Data privacy
Why is running a model locally a stronger privacy guarantee than a policy?
A vendor's privacy policy is a promise about data that has already left your machine. Running the model on hardware you control means the data never travels at all, so the protection comes from how the system is built rather than from trust.
Every protection on the previous pages is a promise. The vendor promises not to train on your data, promises to delete it after a set window, promises that zero data retention does what it says. Those promises may be entirely trustworthy, but they govern data you have already handed over.
Running a model on your own hardware changes the shape of the problem. If the model executes on your laptop or your own server, the prompt and the reply are produced on that machine and never travel to a vendor’s network. There is no log to retain, no policy to read, and no account to audit, because the request did not cross anyone else’s wire. The privacy is a property of where the computation runs, not of a contract you have to take on faith. This is what “structural privacy” means.
Open tooling makes this practical. The project llama.cpp states its main goal is “to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.”[1] Capable open-weight models can run this way on ordinary consumer machines.
One honest caveat keeps this from becoming a slogan. A “local” tool is not automatically an offline one. Some local runners also offer cloud modes, and a model can still reach the network if you connect it to web search or other external tools. The privacy comes from how you actually run it, not from the brand name. If you want the guarantee, run the model on your own hardware and keep it off the network for the work that matters.
Tools for running models locally
- llama.cpp ↗
Open-source engine for running LLM inference on your own hardware, from laptops to servers.
- Ollama ↗
A wrapper that makes it simple to download and run open models locally (it also has cloud modes, so check how you run it).
References
- llama.cpp — ggml-org (GitHub)