Run an open model locally with a tool like Ollama or LM Studio — the model lives on your machine, so nothing you type or generate ever leaves it, and it works with no internet connection at all.
For anyone who cares about privacy — sensitive documents, proprietary code, personal information, confidential business data — running AI locally is the honest answer, and it's more achievable than most people realize. When a model runs on your own hardware, your data never leaves your machine: no prompts sent to a provider, no logs on someone else's servers, no dependence on a third party's privacy policy. And because it's all local, it works fully offline, with no internet connection required. Here's how to do it and what to expect.
The core idea is simple: download an open model to your computer and run it with a local tool. The two friendliest options are Ollama (command-line, one command to download and chat) and LM Studio (a graphical desktop app with a model browser and chat window). Both run on macOS, Windows, and Linux, both download a ready-to-go quantized model for you, and both use your GPU automatically if you have one. Once the model is downloaded, you can disconnect from the internet entirely and it keeps working — the model is on your disk, running on your hardware, answering from its own weights.
What makes this genuinely private is that there's no network step in the inference. A hosted service has to send your prompt to its servers to generate a reply; a local model computes the reply on your own machine, so there's nothing to intercept, log, or leak. For sensitive use cases this is categorically different from any cloud service, no matter how good that service's privacy promises are — with local, there's no data leaving to protect in the first place. This is why regulated industries, security-conscious developers, and privacy-minded individuals gravitate to local models.
The local API feature extends the privacy to your other tools. Both Ollama and LM Studio can run a local server that speaks an OpenAI-compatible API, so you can point existing apps, scripts, coding assistants, and editor extensions at your local model instead of a cloud service. Your code, documents, and queries flow to localhost and stay there. This turns local AI into a private, drop-in backend for whatever you're building or using, at zero per-token cost.
What to expect on capability and cost. The honest trade-off is that you're limited by your own hardware: the model has to fit in your VRAM (or a Mac's unified memory), so very large models may not fit or may run slowly, and the best model you can run privately may not match the very top hosted models. For the vast majority of everyday tasks, though, a good local model in the 7B-32B range is more than sufficient — and it costs effectively $0 in compute beyond electricity, with no per-token meter and no usage limits. So privacy and low cost usually come together in the local route.
A few practical tips for a fully private setup. Choose a model sized for your hardware so it runs comfortably (see the VRAM guides). Download it while online, then you can go fully offline. Keep in mind that the model's downloaded weights are what it knows — a purely local model has no live internet access, so it can't look things up; if you need current information privately, you can pair it with a local retrieval setup (your own documents as embeddings) rather than a web connection. And prune models you're not using, since they take real disk space.
The result is AI that's genuinely yours: private by construction, offline-capable, unlimited, and effectively free to run. For sensitive work that's not just a nice-to-have — it's the whole reason to prefer open models over hosted services.
Spanvero helps you find a model that runs well and privately on your specific hardware. Use /calculator/ to check what fits your machine and see the honest $0-local cost; browse models by your VRAM at /models/8gb-vram/, /models/16gb-vram/, or /models/24gb-vram/; and get started with the guide at /learn/how-to-run-your-first-local-model/. For the tools themselves, see /learn/ollama/ and /learn/lm-studio/.
How do I run my first local AI model? · Ollama · LM Studio · Is running AI locally cheaper than ChatGPT? · Do I need a GPU to run local AI? · Embeddings · VRAM · Local vs API vs renting a GPU
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