Private AI that runs in your company, not in the cloud
On-premise language models on hardware in your own office.
Your documents stay in your network.
Contracts, offers, customer data and internal documents end up in free AI chats every day — outside any company control. You can reverse that without banning anything: run the language model on hardware that sits in your own office.
We're ailone, a small company from Warsaw, Poland (EU). We work remotely with clients worldwide.
AI in the cloud
- Every document and email goes to the provider's servers
- The subscription grows with every user and every token
- The provider, not you, changes the terms, prices and model access
- In regulated industries it's often not allowed at all
- It doesn't know your procedures, contracts or customer history
AI in your company
- Data never leaves your network — it has nowhere to go
- You pay for the hardware and the setup; the system stays yours
- You decide on the model, the version and who has access
- Processing in your own infrastructure — GDPR-friendly
- It knows your documents through a local knowledge base (RAG)
Remote setup and consulting, worldwide
We don't sell licenses or API access. We get local AI running on your hardware — remotely — and help you decide what to run.
Remote setup and configuration
- NVIDIA DGX Spark and ASUS Ascent GX10 — from first boot to a working model
- Clusters of 2–4 units linked over ConnectX-7, for models above 128 GB
- Local models on a server or on your team's PCs with NVIDIA GPUs (e.g. Bonsai 2 27B)
- A chat window for your team and an API for your own tools
Consulting
- Which hardware: one unit, a cluster, or the PCs you already have
- Which open models fit your tasks, your languages and your memory
- How to keep company data inside your own infrastructure
- Answers based on hardware we run ourselves, not on spec sheets
We run what we write about
Our guides come from hardware on our own desks — a two-node GB10 cluster we use every day — and every number says whether it was measured or estimated.
Two ASUS GX10 units (the DGX Spark platform) on one 200 Gb/s cable.
MiniMax M2.7, a 230-billion-parameter model, measured on that cluster.
GLM 5.3 Flash — the model we work with every day, fully local.
Hands-on guides from our own setup
We're adding English versions of our guides one by one.
DGX Spark cluster
Two nodes, 256 GB: how to connect them, scale to four, and our vLLM benchmarks with a 230B model.
Read the guide ->NVIDIA DGX OS 7 and 8
Versions for DGX Spark, why not to upgrade to DGX OS 8 yet, and where the recovery image comes from.
Read the guide ->Bonsai 2 27B on a regular PC
A 27B model in a 6–7 GB file. Our test on eight hardware classes, real speeds and quality, and a setup guide.
Read the guide ->AI decision models
Models that pick an answer instead of writing one: Jev, Clef, Strands Decider, pplx-decider, Nimble and Kev, and which run locally.
Read the overview ->AI demand forecasting
Tested on real public data: call center staffing with Erlang C on NYC 311 (−20% excess cost), warehouses, energy and anomalies.
See the results ->ASUS GX10 vs DGX Spark
Same GB10 chip and 128 GB — what's actually different, and which one to buy at October 2026 prices.
Read the article ->DGX Station vs a Spark cluster
748 GB and 7.1 TB/s for about $92,550, or 256–512 GB for a fraction. Price, memory and speed compared.
Read the article ->DGX Spark setup
From the first-boot wizard to Ollama, ComfyUI and a tool your team uses — and where DIY setups stall.
Read the article ->On-premise LLM vs cloud AI
Model retirements, rate limits and per-use bills vs a file on your own drive — how to calculate which pays off.
Read the article ->DeepSeek V4 Flash on 2× DGX Spark
How we run the 284B model locally with vLLM and DSpark: 44–53 tok/s in daily use, 153.5 tok/s total, real-document tests.
See the benchmarks ->GLM 5.3 Flash: NVFP4 vs EXL3
One model, two engines on 2× DGX Spark: 24–32 tok/s on vLLM (all MIT) vs 36–71 tok/s on TensorFold.
See the benchmarks ->Qwen3.8-Flash-Next on DGX Spark
The fastest model on our cluster: 150.7 tok/s total, stock vLLM, and why vLLM 0.31 was 30% slower.
See the benchmarks ->Best LLM for 2× DGX Spark
DeepSeek V4 Flash vs GLM 5.3 Flash vs Qwen3.8-Flash-Next: speed, memory, images, licenses and seven lessons.
Read the comparison ->Frequently asked questions
How is local AI different from ChatGPT?
ChatGPT runs on the provider's servers, so every document, email and question leaves your company. Local AI is a language model running on hardware in your own office. Conversations and documents stay inside your network — they have nowhere else to go.
Do I need my own server room?
No. For most companies a compact GPU unit that sits in the office is enough — no rack and no server-room cooling — for example NVIDIA DGX Spark or ASUS Ascent GX10. For bigger models, two to four units can be linked into a cluster.
Does it work without the internet?
Yes. The model runs locally, so it keeps answering even without an external connection. The same setup works in environments cut off from public networks (air-gapped).
Is local AI GDPR-friendly?
Processing happens entirely in your own infrastructure, with no transfer to an outside provider and no data leaving the EEA. There is also no risk that your content is used to train someone else's model.
Can you help if we're outside Poland?
Yes. We're based in Warsaw and work remotely with clients worldwide: we set up and configure DGX Spark, ASUS GX10, clusters and local models remotely, and advise on what to run.