If you want to use AI without your prompts ever leaving your computer, a local LLM is the answer. Running a large language model on your own machine means your input is processed on-device and never sent to the cloud — the opposite of ChatGPT, Claude or Gemini. This guide covers why local is more private, which tools and open-weight models to choose for privacy, the hardware you need, and the honest trade-offs.
The short answer
Run the model locally and your data stays with you. Tools like Ollama or llama.cpp load an open-weight model onto your hardware and do all the processing there — no account, no upload, works offline. With cloud chatbots, every prompt is transmitted to the provider's servers. For private chat — legal, medical, proprietary code, personal notes — local inference removes that exposure entirely.
Why local is more private than ChatGPT or the cloud
With a cloud service, your prompt — and anything you paste into it — travels over the network to the provider's servers to be processed. Unless you have opted out, that input can be used to train future models. You also need an account, and the data is retained on someone else's infrastructure.
A local model flips all of that:
- Nothing leaves the device. Your prompts and documents are processed on your own CPU/GPU.
- No account, works offline. Pull the model once, then use it with no internet connection.
- No training on your data. The model is a static file; inference does not send your input anywhere.
That makes local the natural choice for anything confidential — and it is why people running Ollama reach for it on sensitive work.
The tools to run a model locally
You do not run weights by hand — a runtime does it for you:
- Ollama — the simplest CLI. One command (
ollama run llama3.1) downloads and runs a model. Open-source, no telemetry. - LM Studio — a friendly GUI for people who prefer clicking over the terminal.
- llama.cpp — the lightweight, open-source engine many tools are built on; maximum control.
- GPT4All and Jan — other desktop apps that bundle models and a chat interface.
Ollama and llama.cpp are open-source and do not phone home, which makes them the safest defaults for privacy. For a full walkthrough, see what Ollama is.
Which open-weight models to choose for privacy
Any open-weight model you run locally is private — the inference happens on your machine. The real choice is capability versus what your hardware can hold. The strong families that run locally without telemetry:
| Model | Size | Typical RAM (4-bit) | Good for |
|---|---|---|---|
| Mistral 7B | 7B | ~6–8 GB | Light laptops, fast everyday use |
| Llama 3.1 8B | 8B | ~6–8 GB | Best balance on consumer hardware |
| Gemma 2 (Google) | 9B / 27B | ~8 GB / ~20 GB | Quality drafting, summarising |
| Qwen 2.5 | 14B / 32B | ~12 GB / ~24 GB | More capable, needs more VRAM |
| Phi (Microsoft) | small | ~4–6 GB | Very small machines |
| DeepSeek | varies | varies | Reasoning-leaning open weights |
Practical pick: on a typical laptop, Llama 3.1 8B or Mistral 7B quantized to 4-bit is the sweet spot. With a stronger GPU, Qwen 2.5 14B/32B or Gemma 2 27B give you more capability while still running fully offline.
Hardware: what you need (and quantization)
Requirements scale with the model's parameter count:
- Small (3–8B): run on a modern laptop with 8–16 GB of RAM, on CPU or a modest GPU.
- Large (70B): need a powerful GPU (24 GB+ of VRAM) or they run slowly.
The lever that makes this practical is quantization — storing the model's weights at lower precision, typically 4-bit, which cuts memory needs dramatically with only a small quality hit. It is why an 8B model fits in roughly 6–8 GB instead of much more. Start with a small quantized model, see how it performs, and scale up only if your hardware allows.
The honest trade-offs
Local is more private, but it is not free of compromises:
- Less capable. Local 7–32B models trail the frontier cloud models (GPT-5, Claude) on the hardest reasoning and longest-context tasks.
- Slower. On consumer hardware, generation is slower than a hosted API answering from a datacenter.
- You manage updates. Pulling new model versions and keeping your tool current is on you.
For private, sensitive or offline work, the trade is usually worth it. For peak capability on a hard one-off problem, cloud still leads — many people use both. If your goal is keeping data on-device, see AI and data privacy.
The caveat: make sure the tool does not phone home
The privacy of "local" depends on the tool not transmitting anything, not just the model. Ollama and llama.cpp are open-source and do not send usage data. Some GUI apps have optional telemetry — check the settings and turn it off. Downloading model weights from Hugging Face is fine and normal; that is a one-time transfer, and the inference stays local. Verify the runtime, and your prompts genuinely never leave the machine.
The bottom line
A local LLM is the most private way to use AI: your data stays on your device, it works offline, with no account and no training on your input. Pick an open-weight model (Llama 3.1 8B or Mistral 7B to start), run it with Ollama or llama.cpp, use 4-bit quantization to fit your hardware, and verify the tool has no telemetry. It will not match the frontier cloud models on the hardest tasks — but for confidential work, that is a trade worth making. If you want the best model to pair with it, see the best local LLM for coding.
To go further, learn the runtime in what Ollama is, pick a model in the best local LLM for coding, and read why on-device matters in AI and data privacy.
Editorial guide based on the documented behaviour of local LLM runtimes (on-device inference, no network transmission) versus cloud chatbots (input sent to provider servers, possible training use unless opted out), the documented memory effects of 4-bit quantization, and the documented capability gap between local open-weight models and the largest hosted models. We state plainly that local models trail the frontier on the hardest tasks and that some GUI apps carry optional telemetry. No vendor relationship influences this guide.
Related guides: What Is Ollama?
