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