Finetune any open-weight model - Qwen, Llama, Mistral, DeepSeek, Gemma - on your own data, and run it on hardware you control: from a single GPU to a datacenter. Your data never leaves your walls. Predictable cost, no per-token rent. Train once, own it for good.
How it works
A repeatable pipeline that turns your real-world data into a specialized model you fully own.
Your handbook, codebase, tickets or records - and any open-weight base that fits your needs and budget.
An automated training pipeline (SFT + RLVR) turns the model into an expert at your work - reproducible, not black-box.
Deploy on your own GPU or cluster. It stays yours, runs offline, and never sends your data anywhere.
One pipeline, any scale. A compact model on the hardware you already have, or a large model on serious iron - the same craft, sized to what you run and what you can spend.
See it for business →Explore
Each block leads to a dedicated page.
Why we exist and where we're headed: sovereign AI that companies own and operate on their own infrastructure.
Read the vision →The pipeline - data curation, finetuning (SFT/RLVR), and deployment - and how you run models of any size.
See the mechanism →Turn your data into private, specialized models on your own infrastructure - at any scale, from a single box to a datacenter.
For business →By default your data stays in your network - local-first, offline. One reason teams choose Quantinia, among many.
About privacy →Flat and transparent - a predictable fee next to what a frontier model would cost you per token.
See the costs →What you need to train and run - from a 24 GB card to a GPU cluster. NVIDIA and AMD, both first-class.
See the hardware →Everyday work runs on your own finetuned model, locally. Escalate to a frontier model only for the rare ~1% of genuinely hard cases - and pay for just those.
Talk to us →