Run a private language model on your own EU server

Someone in administration pastes a draft of a customer contract into a free chat service and asks for a summary. It saves him half an hour and nobody notices. Two rooms down, a colleague does the same with a personnel file. You know it happens, you do not know how often, and you certainly do not know where that text ends up. A language model of your own takes that temptation away, because then there is a place where it is allowed.

Why your own model in the EU

Most language models you know run on servers outside Europe. Everything you type into them goes along: the proposal, the letter to a client, the email from an angry customer. For many organizations that is not sustainable. An accounting firm, a healthcare provider, and a municipality all have agreements with their clients or their regulator about where data may be, and a chat window in a browser rarely falls under that.

With private hosting the model sits on a machine you rent or own, in a data center in the EU. The model's weights sit on that disk, your employee's question goes there, and the answer comes back. There is no third party reading along, because there is no third party. That makes a question about the GDPR or an audit a lot shorter: you can point to where the machine is and who can access it.

What an open model can and cannot do

Open models have become good in recent years at exactly the work offices use them for. Summarizing a report, rewriting a letter into plain language, answering a question from two hundred pages of your own documentation, sorting an incoming email to the right department. For that kind of task your colleagues notice little difference in practice from a paid chat service.

With the heaviest tasks you do notice. Complex reasoning over many steps in a row, or writing code of any size, still goes better with the largest commercial models. That is why we do not test with examples from a brochure, but with your own documents. If an open model turns out not to handle the work, we tell you right away and look at whether part of the process is better off without AI.

How we set up the hosting

Underneath a language model sits ordinary infrastructure. We set up a machine with a GPU in our managed environments within the EU, install the drivers and the runtime that serves the model, and put a protected API address in front of it. Your own software talks to that address, and your employees work through a simple chat page behind your VPN. Which card and how much memory you need depends on the model and on how many people ask a question at the same time. We work that out before you rent anything, and the bare computing side we arrange through AI hosting (GPU).

Within 10 business days there is a working prototype your colleagues can ask real questions. Only then do you know whether the model is big enough and whether the server answers fast enough. Monitoring of memory, temperature, and availability runs along from that moment, with an agreed escalation if something stops. For bringing it back we connect to Backup and disaster recovery, so a broken machine does not mean a broken service.

Access, logging, and ownership

A model on your own ground is not safe by itself. We arrange who may use it, per user or per department, and record how long questions and answers are kept. Sometimes you want to log everything to be able to account for your work; sometimes you want to keep nothing because client data passes through. That is a choice you make yourself and that we record in the setup, not something we fill in for you.

You are the owner. The model, the settings, the documents, and the logs are yours, and the documentation describes exactly which version runs and why it was chosen. If you want to place it with another party in a year or manage it yourself, you take that folder with you and it simply works. We do not build a lock on the door that only we have the key to.