SustainedCloudWhat does it take to run AI on your own hardware?
Local AI means running language models and assistants on servers you own, instead of sending your data to a US cloud service. Open-weight models (Llama, Mistral, Qwen and others) have become good enough for everyday business tasks: drafting text, summarizing, and answering questions from your own documents. What you need is not a data center, but a correctly sized GPU server and clarity about the use case.
Why local at all
Customer data, contracts and internal documents never leave the house, which turns the hardest data protection questions into non-questions: no third-country transfer, no provider terms to audit, no per-seat or per-token bill that grows with usage. A local model costs the same in month twelve as in month one, and it keeps working when your internet connection does not.
The hardware, realistically
The deciding factor is GPU memory. A team assistant for a company of 10 to 50 people runs well on a single server with one modern GPU carrying 24 to 48 GB of VRAM; document search (RAG) adds fast NVMe storage and RAM more than GPU power. Typical investment is in the low five figures, with power and operations adding a few hundred euros per month. Bigger teams scale by adding GPUs, not by changing the concept.
The data question
You need less than you think. Nobody trains a model from scratch: with RAG, the model reads your existing documents at question time, so your file server is the dataset. What matters is housekeeping, current versions, sensible folder structure and access rights, not volume. Fine-tuning a model on your own data is rarely the first step and often never needed.
Calculator
What would local AI need at your company?
Hardware
1 GPU server · 24-48 GB VRAM
One-time investment
€8,000 to €15,000
Power & operations
€150 to €300 / month
Data
Your existing documents are enough: no training, no extra data. Add fast NVMe storage and RAM for the search index.
Indicative values for open mid-size models. Exact sizing depends on the use case.
Get in touchFAQ
Common questions.
Are local models as good as ChatGPT?
For open-ended general knowledge, the biggest cloud models still lead. For grounded company tasks, answering from your documents, drafting, summarizing, a well-set-up mid-size model is usually sufficient, and the gap narrows with every model generation.
What about our data protection obligations?
Local processing removes the third-country transfer question entirely, which is the hardest part of using US AI services under the GDPR. Access control, logging and retention remain your responsibility, exactly as with every other internal system.
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