Is there AI that doesn't send your data to the cloud?
Yes. It runs on a box you own.

Yes. Capable AI language models can run entirely on a computer in your office — or, for lighter work, on a single laptop — with nothing crossing your internet connection. It's the same kind of technology as the chatbots you've tried, minus the part where your text travels to somebody else's data center.

How it works, without the jargon

An AI model is a file. A large one — tens of gigabytes — but a file, the same as a video. Cloud providers keep their model files on their servers and rent you access. "Open-weight" models are different: the publisher releases the file, and anyone can download it and run it on their own hardware.

The hardware that matters is the graphics card. The model loads into the card's memory, your question goes in, the answer comes out. No account, no API key, no monthly meter. Once the file is on your disk, the internet connection is optional.

What actually fits on one card

We can be specific, because this website is the example. The assistant you can open at the bottom of this page runs on one 24GB graphics card in our office. Here's what that card is doing:

• The model: 27 billion parameters, open-weight, compressed to 4-bit precision so it fits.
• Memory: about 23 of the card's 24 gigabytes in use, with room to read roughly 32,000 tokens at once — a long contract, or a thick stack of emails.
• Speed: the first word appears in about a seventh of a second, and text streams at around 44 tokens per second. That's faster than you read.

That is one card, in one quiet tower, serving a public website. For an office, the sizing question isn't how many people have access — it's how many are generating at the same moment. A 25-person office rarely has 25 simultaneous requests. We wrote up how to measure that before you buy in our guide to right-sizing a server.

What broke the last time we did this

Local AI is real infrastructure, and infrastructure has bad days. From our most recent model upgrade, on our own server:

• The model thought itself into silence. Newer models can "reason" before answering. Ours spent its entire response budget reasoning and returned nothing visible at all. One startup setting fixed it — once we knew which one.
• A feature we weren't using ate the memory. The model could also read images. That capability quietly reserved memory on the card, leaving too little for actual conversations. We turned it off.
• A driver update split the system in two. One half of the server updated its graphics driver and the other half didn't. The GPU monitoring tools stopped working until both matched.

None of these were hard to fix. All of them would have been a bad afternoon for an office manager with no one to call. That's the honest case for having an integrator: not that the technology is mysterious, but that somebody should have already hit the wall on their own equipment.

The trade-offs, stated plainly

• It isn't the frontier. The very largest cloud models are still better at open-ended reasoning and obscure knowledge. For summarizing, extracting, drafting, and answering from your own documents, the gap is small enough that most people stop noticing.
• You own the upkeep. Models improve; someone has to evaluate and swap them. Drivers update. Disks fill.
• It's a physical object. It draws power and makes heat. A single-card tower is happy under a desk. Larger systems need an electrical and cooling check before they arrive — we do that as part of the process, and can coordinate with a licensed electrician when it's needed.
• You pay up front. Which is also the advantage: no per-seat fee, and no bill that grows with your headcount.

What it costs

Our entry inference servers — one workstation-class GPU in an office-friendly tower — run $1,500 to $7,500. The spread is mostly the graphics card, and component prices move with the market. Builds using quality used parts sit at the low end. Multi-GPU rackmount systems for heavy concurrent use run $5,000 to $25,000 and up. Ongoing support starts at $200 a month if you'd rather not own the upkeep yourself.

At the very small end, you don't need a server at all. Archivist, our Windows desktop app, runs a smaller model entirely on one laptop. The base version is free, and it's the cheapest way to find out whether local AI earns its keep in your workflow.

The unplug test

Plenty of products say "local" or "private" and mean something softer — a desktop app that still calls a cloud service, or a "private" tier on shared servers. There's a simple way to check any vendor's claim, including ours: pull the network cable and ask it a question. If it answers, it's local. If it spins, it isn't.

If you're in Southern California

We build these servers and install them on-site across Los Angeles, Orange County, the Inland Empire, and San Diego — sized to your team, set up on your network, with your staff shown how to use it. We'll ship a configured system elsewhere. Either way it starts with a few questions about what you want the AI to do and how many people will use it at once.