---
title: Private AI, On-Premise LLM & GPU Services | Ordinexis
description: Plan and run AI on your own servers or private cloud. Get NVIDIA H200, Linux GPU, local LLM and air-gapped deployment support from Ordinexis.
url: https://ordinexis.com/services/gpu-infrastructure
---

Ordinexis expertise

# Private AI & GPU infrastructure. NVIDIA H200 servers and local AI models on your own site

Your AI. Your servers. Your data stays home.

Keep your AI, and your data, in your own hands. We help organisations run AI on their own servers, private cloud or local devices. That covers GPUs (the specialised processors used for AI), local large language models (LLMs), Linux setup and access controls, plus support for model training, testing, voice and image analysis.

[Talk about your project](https://ordinexis.com/contact#gpu-infrastructure)

Choose where your data stays

- 01 **Your data** Documents, business systems, voice and vision.
- 02 **Your AI environment** Run local models with access rules and monitoring.
- 03 **Your people** Keep answers and tasks within your chosen environment.

Your own servers / Private cloud / Offline network

Capability

## Choose the hardware. Set it up. Keep it running.

We work on the hardware, Linux servers, network, AI models and connected applications. We also help monitor access, performance and costs.

### NVIDIA H200 and GPU systems

Estimate the computing power your work needs. Plan and set up servers or groups of servers, check capacity and improve performance.

### Private model serving

Run AI models through private software connections. Choose how requests reach each model, control access and monitor capacity, faults and costs.

### Local deployment patterns

Use private cloud, your own site or local edge devices. Combine setups where needed. Limit outgoing network access or keep the environment fully offline, known as air-gapped.

When it makes sense

## When does private AI make sense?

### Data must stay in your environment

Regulation, contracts, security, or internal policy requires local processing.

### You need steady response times or local access

The work needs a predictable response time or must continue when external connections are limited.

### Usage is large or stable

Dedicated hardware may offer more control or better value for steady, high usage. Compare hardware, electricity, support and model costs with a hosted service.

### You need local ownership

Your team needs direct control over models, updates, logs, access, and operating schedules.

Before you decide

## Questions about private AI and GPUs

Answers about uses, testing, setup and cost.

### When should we consider on-premise AI instead of hosted models?

Consider on-premise AI when data must stay on your servers, local access matters or usage is large and steady. Your team also takes responsibility for hardware, updates and availability. Compare the full operating cost and support needs with hosted AI before deciding.

### Do you help with GPU sizing and local LLM deployment?

Yes. Ordinexis helps estimate GPU capacity, set up NVIDIA H200 and Linux systems, and run local large language models (LLMs). We also help with model access, capacity planning and performance. Choose hardware based on the models, users and response times you need.

### What should we provide for an infrastructure proposal?

Share the models you plan to use, expected users, data restrictions and existing hardware. We can plan setup, access controls, monitoring and staff handover. Include hardware, model licences and ongoing support when comparing costs.

Demo, pricing or project advice

## Let’s see what AI can do for you.

Tell us what’s slowing your team down. We will show you a demo, give you a price and suggest the fastest way to start.

[Book a demo](https://ordinexis.com/contact) [Email us](mailto:hey@ordinexis.com)