Running AI locally means downloading an open model and running it directly on your own computer, so it works offline, costs nothing per use, and keeps your prompts off the cloud. In 2026 the easiest path is a tool called Ollama: you install it once, run a single command, and a small but genuinely useful model starts answering you in a terminal or chat window. The main trade-offs are speed and capability, which depend on your hardware. This guide walks through what you need, how to set it up, and which models make sense for a normal machine.
What you need to run AI locally
Local models run on the memory and processor you already have, so the question is what fits.
| Hardware |
What it can run |
Experience |
| Laptop, 8 GB RAM |
Small models (1-3B) |
Works, basic answers |
| Laptop, 16 GB RAM |
Mid models (7-8B) |
Good for most tasks |
| Mac with unified memory |
7-14B comfortably |
Fast, low setup |
| Desktop with a recent GPU |
14B and up |
Closest to cloud feel |
Two terms matter. Parameters (the "7B" means seven billion) roughly track capability and size. Quantization shrinks a model so it fits in less memory with a small quality loss; most local models you download are already quantized. If you are unsure what these models even are, start with what an open-source LLM is.
Setting it up with Ollama
The fastest reliable route in 2026:
- Install Ollama from its official site for your operating system. It runs as a background service.
- Pull a model with one command, for example a small general model. Ollama downloads it once and caches it.
- Chat by running the model name; type your prompt and it answers locally. No account, no internet after download.
- Add a friendlier interface if you want a chat window instead of a terminal; several free front-ends connect to Ollama.
If you prefer a graphical app from the start, a few desktop tools bundle the model and a chat UI together. The setup principle is the same: download once, run offline.
Choosing a model
Pick for your task and your memory, not for the biggest number.
- General chat and writing: a 7-8B instruction-tuned model is the sweet spot on 16 GB.
- Coding help: a code-specialized model of similar size gives better completions.
- Tight hardware: a 1-3B model still summarizes, drafts, and answers simple questions quickly.
Local models trail the best cloud models on hard reasoning, and they have a fixed knowledge cutoff, so they will not know recent events. For private, everyday work, that is often a fair trade for free and offline.
What to skip
- Skip oversized models. A model that barely fits will swap to disk and crawl. Leave headroom.
- Skip expecting cloud-level answers. Local models are capable, not identical to the frontier. Match expectations to size.
- Skip buying a GPU before you try. Test on what you own first; many people are happy on a mid-range laptop or a Mac.
- Skip random model downloads. Stick to well-known, widely used open models so you get tested quality and clear instructions.
FAQ
Is running AI locally free?
The models and tools like Ollama are free. Your only cost is the electricity and the hardware you already own. There is no per-message or subscription fee.
Do I need a powerful GPU?
No. A modern laptop with 16 GB of RAM, or a Mac with unified memory, runs useful 7-8B models. A GPU mainly makes larger models faster, not required.
Is local AI more private?
Yes. Your prompts and files stay on your machine and are not sent to a provider, which is the main reason people run models locally for sensitive work.
Will a local model know about recent events?
No. It only knows what was in its training data up to its cutoff and has no live internet access unless you add tools yourself.
Where to go next
Compare the best local AI models, understand what an open-source LLM is, and learn what a knowledge cutoff means.