On-premise neural network on your own server
Corporate AI that runs without internet access and never sends your data outside. We deliver it turnkey: hardware, models, integrations, support.
Why cloud AI doesn’t work for business
ChatGPT and cloud services are convenient — right up until trade secrets and personal data enter the picture.
Your data goes to someone else’s cloud
Contracts, personal data and correspondence get shipped off to a vendor’s servers. For businesses bound by data-protection law — banks, healthcare, legal — that’s simply not an option.
You pay for every single request
Cloud models bill by the token. The more your team uses it, the bigger the invoice — and there’s no ceiling on it.
You’re at the mercy of access staying open
An outage, a policy change, a payment or sanctions issue — and your corporate AI just stops working.
Your own server with a neural network inside your perimeter
We install a language model on your own hardware. Staff work through a familiar chat, but every request is processed locally — not a single character leaves for outside services. The server can be fully disconnected from the internet, and the neural network keeps working.
- Server spec matched to your workload and number of users
- Installing and tuning the models (text, vision, speech-to-text)
- A web chat interface for staff, with access control
- Connecting it to your knowledge base and documents (RAG)
- Integration with 1C, CRM, Telegram — as needed
- Staff training and ongoing technical support
When you need a heavier model — your data still doesn’t leak
Routine work stays with the local model inside your perimeter. But when a task needs a top-tier model (a heavy analysis, rare expertise), the request goes outside only after double sanitization of personal and confidential data.
Local model, no internet
- The server is physically isolated (air-gapped) or locked down by a firewall
- Handles 80–90% of routine tasks: documents, replies, knowledge-base search
- Personal data and trade secrets never leave the loop
A top-tier model via API — with sanitization
- The request is stripped of names, phone numbers, addresses, account and payment details
- Sensitive entities are replaced with placeholders (masking)
- The answer comes back into the loop and the data is restored locally
A double sanitization check — before a request ever leaves
PII detector
Rules and dictionaries find names, phone numbers, e-mails, tax IDs, account numbers, addresses, contract numbers
Model review
The local model re-reads the text and catches what the rules missed — context-based leaks
Masking
Findings get replaced with tags like [CLIENT_1], [INVOICE_2]. Only the anonymized text goes out
Audit log
Exactly what left for the external API gets logged. You can audit it after the fact and revoke access
If the detector or the model is unsure, the request never leaves — it’s handled locally instead. The policy can be tightened to a full ban on external calls for specific departments.
What people actually do with an on-premise AI server
One server covers several departments at once — one model, many scenarios.
Knowledge base & procedures
Search internal documents, answer “how do we do this here”, summarize a 200-page manual instead of making anyone read it
Legal department
Contract analysis, redline comparison, risk spotting, drafting standard documents from your own templates
Accounting & document flow
Reading invoices and delivery notes from photos, reconciling totals and VAT, exporting to your accounting system
Sales & proposals
Drafting commercial proposals, answering client inquiries, scanning correspondence for agreed terms
Customer support
Answering routine questions from your own docs across the site and messengers, escalating the hard cases
HR
Screening résumés against your criteria, answering staff HR questions, drafting job posts and offers
Development
An in-house code assistant: source code never leaves, works with your own codebase and conventions
Engineering & production
Search technical docs and standards, help specifying equipment, parse procedures and datasheets
Analytics
Reports in plain language instead of spreadsheets, spotting anomalies, answering “why did sales drop in May”
Content & marketing
Articles, product descriptions, newsletters, translations — all in your brand’s voice
Calls & telephony
Call transcription, summaries, quality control, automatic CRM entries
Security & logs
Parsing logs and incidents, explaining suspicious activity in plain language
What each model class can do, and how much VRAM it needs
The main resource for on-premise AI is video memory (VRAM). Below are real-world figures for quantized models — the form they actually run in production.
| Model class | VRAM | What it handles |
|---|---|---|
| 7–9B | 6–8 GB | Chat, summarization, classification, simple knowledge-base answers. Fast and cheap, but loses the thread on long instructions. |
| 12–14B | 9–12 GB | Solid with documents, email triage, text drafts, basic code. The practical floor for a business deployment. |
| 27–32B | 18–24 GB | The workhorse: contract analysis, complex instructions, image recognition, long context. Our default recommendation. |
| 70B | 40–48 GB | Close to top-tier cloud models on most business tasks. Needs two GPUs or a professional accelerator. |
| 100B+ (MoE) | 80 GB and up | Top-tier reasoning quality. Server-grade accelerators, dedicated cooling and power. |
Note: the figures above are for one or two concurrent users. With dozens of parallel requests, memory gets used up faster, and the headroom is sized separately.
What a server for your task costs
Turnkey estimates: hardware, assembly, model setup, tuning and launch. The final quote depends on exchange rates, GPU availability, and your fault-tolerance requirements — we price it for the actual task.
Start
- ·Models up to 14B, freely
- ·27B, quantized
- ·Knowledge base, support, text
Working
most popular- ·27–32B, comfortably
- ·Vision + speech-to-text
- ·Contracts, source documents, analytics
Advanced
- ·70B models
- ·Several models running in parallel
- ·Heavy load across departments
Maximum
- ·100B+ MoE
- ·Fault tolerance
- ·A dedicated rack, power, cooling
Already have a suitable server? We deploy on it — then the only cost left is the setup work. A pilot rental of capacity at our facility in Russia is also available.
Desktop server or rack-mount
We build the same neural network in two form factors. The choice depends on where the server will sit and how many people will use it. We’ll help you decide on a free consultation.
Desktop (Tower)
A powerful workstation in a floor-standing case. Sits right in an office or a small server room, runs off a regular wall socket, needs no rack or professional cooling.
- ✓No server rack or its supporting infrastructure needed
- ✓Quiet enough to sit right in the office
- ✓Cheaper to buy and to maintain
- ✓Fast to get running: delivered, plugged in, working
- ✓1–2 GPUs — models up to 70B
Rack-mount (4U)
A server build for a 19″ rack. Redundant power, hot-swap drives, professional cooling for round-the-clock load and dense multi-GPU installs.
- ✓Redundant power and fault tolerance
- ✓2–8 GPUs — 70B–100B+ models running in parallel
- ✓Built to run 24/7 under load
- ✓Scales by adding GPUs and nodes
- ✓A proper home in a server room or datacenter
What makes up a “turnkey” price
We work transparently: the quote lists the components at market price separately from our labor. You see exactly what you’re paying for — no “black box” markup.
The cost of assembly and setup is a fixed share of the project budget, known upfront. What you’re paying for is exactly the spec, the error-free assembly and the working model setup — the part where a DIY purchase most often costs weeks and extra money.
“We’ll just buy a GPU and set it up ourselves” — where that usually breaks down
Getting a model running on your own machine really can be done in an evening. The trouble starts once real departments need to work with it every day. Here’s what eats up weeks and quietly kills the project:
Picking the model and quantization
Compress too hard and quality on your tasks collapses; too little and it won’t fit in memory. The right balance comes from testing on your own documents, not from a review site.
Search over your own documents
Answers made up out of thin air instead of grounded in facts is the classic DIY failure mode. Correct document chunking, embeddings and re-ranking are a whole engineering job on their own.
Multiple users at once
What flies for one user grinds to a halt for fifteen. You need queuing, batching, and memory sized for concurrent requests.
Sanitizing data before it reaches external models
If you ever call out to cloud models, you need a sanitization layer with double-checking and an audit trail. A mistake here is a data leak.
Integrations and access rights
1C, CRM, email, Telegram, per-department permissions — this is where “installed a model” stops and “built a system” begins.
Support and updates
Models and libraries get updated, hardware degrades, requirements change. Without ongoing support, the setup is stale within six months.
We’ve been through all of this on our own infrastructure: we run our own server with a local model, and our own products run on it. That’s why we spec configurations not from a manufacturer’s spec sheet, but from how the thing actually behaves under real load.
Who needs a corporate neural network
Banking & finance
Handle client data without ever passing it to a third party
Healthcare
Medical records and scans never leave the clinic
Legal
Review contracts and case files under attorney-client privilege
Retail & services
Customer databases and loyalty programs processed without ever leaving the building
Manufacturing
Technical documentation, procedures, engineering support
IT & development
A code assistant with zero risk of source code leaking out
Frequently asked questions
Related reading
How to build and train your own AI server — step by step on the blog
How to Build an On-Premise AI Server: Step-by-Step Guide
A step-by-step guide to building a self-hosted AI server: picking the model, sizing GPU memory, the software stack, RAG, and security, with real configs and pricing.
How to Train an AI on Your Own Data: RAG Explained
You don't retrain an AI to know your company — you connect it to your documents with RAG, so answers come from real files instead of guesses. Here's how RAG works, step by step.
Best Open-Source LLMs to Self-Host in 2026
Qwen, Llama, DeepSeek, Gemma and Mistral now cover most business work and run on hardware you own. How the families differ, how much VRAM each needs, and how to choose between them.
Own AI Server or Rented GPU: Which Costs Less
Renting GPU time wins for pilots and bursty load; your own server pays for itself in 12-18 months of steady use and gives full control of the data. The comparison in money.
Let’s size a configuration for your task
Tell us how many employees will use it and what documents they’ll work with — we’ll spec a server and quote the price. The consultation is free.
See also: other AI solutions from Stitex