Local AI systems
We choose the hardware, deploy and tune language models that run on your own infrastructure. Your data stays in-house, and there is no per-request bill.
Benchmarks
Numbers from our own systems. We run the same kind of measurements with your tasks before proposing a setup.
We will publish only what we measure ourselves on our own hardware: the model, the settings and the result. For now, the hardware is ready.
Our hardware →Services
Order the whole path or just one part of it.
We compare GPU servers, NVIDIA DGX Spark and Apple Silicon machines on your tasks and models. We measure speed, context length and quality, so you buy what you actually need.
We prepare a server or a Mac, install a model server with an OpenAI-compatible API, and set up access, automatic restart and monitoring.
We pick an open model for your task and get more out of the hardware: quantization, speculative decoding, long context, image understanding.
We connect the local model to what your team already uses: search across company documents, a chat or coding assistant, integrations over an API.
Process
We work out which jobs the AI should do, with what data and under which privacy requirements.
We test several models and hardware options on your tasks: speed, quality and context length. The numbers decide.
We set up the hardware, models, API and access inside your network. The system starts on its own after a reboot.
We monitor the system and try new models as they come out. We upgrade only when measurements show a gain.
Why local
Contracts, customer data or source code stay in your network. No need to negotiate what may be sent to an outside provider.
Costs depend on the hardware and electricity, not on how much your team uses the system.
The model will not change or disappear without your decision. Swap it when a better one comes out.
The largest cloud models are still stronger at some tasks. If local does not pay off in your case, we will say so after the measurements.
FAQ
It depends on the task and the model size. Smaller models run on a single good GPU or an Apple Silicon machine; larger ones need more memory or several devices. We find out with measurements before you buy.
For some tasks yes, for others no. That is why we start with tests on your real tasks and show you the results before you decide.
The model runs on your hardware, in your network. If any part, such as updates, needs the internet, we document it, and that connection can be switched off.
Yes. We measure what it can do and tell you which models and tasks are realistic on it.
Contact
Tell us what work you want to run and what hardware you have or plan to buy. We will suggest what to measure first.
Vilnius, Lithuania · working across the EU · LT / EN / RU