An EU GPU server for your AI model

Your AI model works. It runs on a developer's laptop, or on a GPU you rent by the hour from a big provider. As long as it is a trial, that is fine. But once colleagues lean on it every day, it starts to pinch: the laptop goes home at night, the bill keeps running while nobody uses the model, and the documents you feed it sit on a server in a country you cannot name.

When a GPU is really necessary

A GPU is not a status symbol, and you often do not need one. A small classification model, a search function over your own documents, or a few hundred predictions a day run fine on an ordinary processor. VPS hosting is enough for that, and it saves a considerable amount. A GPU becomes interesting when you want a language model to answer within a second, when you process images or video, or when you train on your own data.

That is the first thing we work out in the free intake: which model, how many requests a day, and how fast the answer has to be there. From that follows whether you need a GPU and, if so, which one. A card with a lot of memory for a large language model is something else than a card for fast image recognition. Choose too big and you pay for idle time. Choose too small and your model waits for memory. If a GPU turns out to be unnecessary, we tell you right away.

Hourly rental or a fixed server

The big providers rent out computing power by the hour. Handy for trying something out, and for a training run of a few days often the smartest choice. But a model that runs every business day uses that card 8 to 12 hours a day. The bill is different every month, and your data sits with a provider outside the EU, with everything that means for your GDPR obligations.

So we put a fixed GPU server for you in our managed environments within the EU, in the location that fits your requirements. If you want to own the hardware yourself, we place your machine through Server colocation. If you run a language model on it for your own staff, Private LLM hosting in the EU usually belongs with it: the same server, set up as a protected assistant.

What we set up for you

A GPU server is not an ordinary server with a card in it. The drivers have to match the runtime, the runtime the model, and everything the operating system. Someone doing that for the first time can easily lose a week to it. We install the operating system, the drivers, the runtime, and the model server that serves your model to your application. Access runs through a VPN or from fixed IP addresses, not through an open port to the internet.

Then you put your model on it and test with real data and real users. The load usually turns out different from what you expected: a peak at 9 AM, silence on the weekend. We measure that and adjust the setup to it. If you already roll out your software with Kubernetes and containers, we connect the GPU server to that, so rolling out a new model works just like the rest.

Monitoring, backups, and ownership

A model in production is only finished when someone notices it has stopped. We monitor usage, temperature, memory, and availability. That monitoring runs day and night. What happens after an alert, we agree with you in advance in an escalation that fits how critical the model is for you. Model files and configuration go into Backup and disaster recovery, so a new machine quickly does the same again.

And the setup is yours. You get the documentation, the configuration, and the scripts the server was built with, so you can manage it, move it, or add a second machine later without us. If you would rather stay with us, the server falls under Managed hosting and we keep it up to date. Both are fine. It starts with a free intake in which we work out whether a GPU is worth it for you.