How do I run a private, local AI assistant on my own device?
To run a private AI assistant locally, you need two layers: a runtime that loads the model (like Ollama) and a friendly app on top (like LM Studio or Jan). Pick a model that fits your machine's RAM and VRAM. Start with one small model and one chat app, and only connect it to your own documents once you trust it. You get full privacy, no per-use fees, and it works offline.
A few years ago, running an AI on your own laptop meant a quality penalty you’d feel in every reply. That’s no longer true. Open models now run genuinely well on consumer hardware, so a private assistant, with nothing leaving your device, is a real option rather than a hobbyist compromise. Here’s how the stack works and how to start without overbuilding.
Know the two-layer stack
Local AI is two pieces, and confusing them is the most common early mistake:
- A runtime. The engine that loads the model file and runs it. Ollama is the popular one; it does the heavy lifting and exposes the model so other apps can talk to it.
- An app on top. The friendly part you actually use. LM Studio gives you a polished GUI; Jan is an open-source chat app. This is the window into the model the runtime is running.
You need both. The runtime without an app is a command line; the app without a runtime has nothing to talk to.
Pick a model that fits your machine
The open models worth knowing in 2026, like Llama, Qwen, DeepSeek, and Gemma, all come in a range of sizes. Smaller versions run on a normal laptop; larger ones want a real GPU. The trick is matching the model to your hardware instead of grabbing the biggest one you’ve heard of and watching it crawl.
There’s a whole guide on this. What AI model can my computer run walks through sizing it properly. The short version below covers the ceiling.
Check the hardware reality first
The real limit is memory: RAM, plus VRAM if you have a dedicated GPU. A model that doesn’t fit in memory either refuses to load or runs so slowly it’s useless. That’s why “what runs well” is so machine-dependent that people write entire 2026 hardware guides about it. There’s no single answer, only what fits your box.
Before you commit, check your RAM and VRAM and pick a model size that lives comfortably under that ceiling, not right at it.
What you get, and what you give up
The payoff is real and specific:
- Full privacy. Nothing leaves the device. Your notes, your inbox, your drafts stay local.
- No per-use fees. Once it’s running, you’re not paying by the token.
- Offline. It works on a plane, in a dead zone, during an outage.
The trade-off is just as honest. The very biggest cloud models can still out-reason a local one on genuinely hard tasks. So the sane stance isn’t local-or-cloud purity. It’s local by default, cloud when you choose to reach for it. The guide to choosing AI tools has a checklist for deciding which work crosses which line.
Give it your own context, safely
A generic assistant is useful. An assistant that knows your material is the point. Tools that index your documents locally, a private “second brain”, let the model help with your real notes, contracts, and files without uploading any of it. The indexing happens on your machine, so the privacy guarantee holds even as the assistant gets personal.
This is also where you want least-privilege thinking from the start: connect read-only first, and add the ability to act only for the specific things you actually want automated. Once an assistant can take actions and not just answer, the cautions in why AI agents get things wrong start to apply to your setup too.
Start tiny and grow
Don’t assemble the whole thing on day one. The order that works:
- Install one runtime and one small model.
- Just chat with it. Get a feel for speed and quality on your machine.
- Once you trust it, point it at your notes.
- Only then wire it into apps and actions.
Each step earns the next. If you skip ahead and it misbehaves, you won’t know which layer to blame.
Where this fits at Physea
Everything above is assemble-it-yourself, and plenty of people enjoy that. Sia is that stack, productized: a local personal AI, so you get the privacy and the no-fee math without sourcing a runtime, a model, and a notes layer and keeping them in sync. If you’d rather build your own, the steps here stand on their own. Sia is just the version where someone else has done the wiring.
Common questions
- What do I actually need to run AI locally?
- Two pieces: a runtime that loads and runs the model (Ollama is common), and an app on top to talk to it (LM Studio's GUI, or the open-source Jan). Plus a model file sized to fit your machine.
- What decides whether a model runs well on my computer?
- Mostly RAM and VRAM. Memory is the real ceiling. A model that doesn't fit will either refuse to load or crawl. Smaller models run on laptops; larger ones want a strong GPU.
- Is a local AI as good as ChatGPT?
- For everyday tasks, modern open models get close. For the hardest reasoning, the biggest cloud models can still pull ahead. A common setup is local for private or routine work, cloud when you choose to reach for raw horsepower.