Private LLM for Business
Your own large language models on your own hardware. No OpenAI. No Google. No vendor lock-in. IRIDIUM™ Private Server: full data sovereignty, full model control, zero dependency.
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Growth systems, campaigns, and
creative projects implemented.
y
Experience in production, advertising, and
digital marketing.
Founder-led
You’ll work directly with the founder.
No account managers. No outsourced strategy.
End-to-End
Strategy, creation, production, and
meta-advertising—all in one system.
Problem
Why your business doesn’t own its AI — even though it’s paying for it
Every business using ChatGPT, Copilot or Claude through the cloud is paying for a service it doesn’t own. Every request goes through a US provider’s servers. Every response is generated on infrastructure you don’t control. And every prompt — including the business data you enter — is processed under terms of service the provider can change at any time.
That works as long as AI is treated as a toy. For occasional text generation, a quick summary, a phrasing suggestion. But once AI becomes a real part of your business processes — once customer data is processed, proposals generated, internal knowledge bases queried — the dependency becomes a risk.
The risk has three dimensions. First: data privacy. Your data sits on foreign servers, in foreign jurisdictions, under foreign control. Second: dependency. The provider can double prices, change the API, retire the model or tighten usage policies — and you can do nothing but pay or migrate. Third: control. You don’t know which model is running, how it was trained, whether behavior has changed or why your results suddenly look different than last week.
A private LLM server eliminates all three. Your data. Your models. Your infrastructure. Your system.
Solution
IRIDIUM™ Private Server — LLMs that belong to you
An IRIDIUM™ Private Server is a fully self-contained AI system running on dedicated hardware — in your business or in a European data center exclusively assigned to you. The language models running on this server belong to you. No API costs per request. No third-party terms of service. No dependency on a single model or vendor.
In practice: you operate powerful open-source LLMs — Llama, Mistral, Qwen and specialized models — on hardware under your physical or contractual control. Your team uses these models through custom frontends we build for your workflows. The models can be fine-tuned on your company data. And when a better model appears, you switch — without migration, without contract termination, without data loss.
The decisive difference from any cloud solution: with a private server, you’re not a user. You’re an operator. The model runs for you — not for millions of other users simultaneously. The compute belongs to you. Response times are consistent. And nobody between you and the model reads along, trains on your data or decides what the model is allowed to do.
How it works
How we build your private LLM server
01
Requirements definition and model strategy
We start with your use cases. Which tasks should the LLM handle — text generation, code creation, document analysis, customer communication, knowledge queries? How many users work with the system simultaneously? What response quality and speed is expected? From that, we determine which models are optimal and what hardware configuration they need.
02
Hardware and infrastructure
Based on requirements, we configure the server — GPU selection, memory, storage capacity, network connection. Either as a physical system for your location or as a dedicated instance in a European data center. Hardware is procured, configured and tested before going into operation. You get a system that’s productive from day one — not a tinkering project.
03
Model deployment and fine-tuning
We deploy the selected models on your server. Where needed, models are fine-tuned on your company data — so they know your terminology, understand your document structure and deliver responses that fit your business. RAG systems are set up so the LLM can access your internal documents, knowledge bases and data sources without embedding that data into the model itself.
04
Frontend, integration and handover
Your team needs interfaces, not terminals. We build user interfaces for every use case — chat interfaces, search masks, admin panels, API endpoints for integration into existing software. The system is documented, your team is onboarded and operations are handed over — to your IT team or to us, depending on how you want to organize ongoing operations.
What’s included
What an IRIDIUM™ Private Server covers
✓ Dedicated hardware: GPU server configured for your models and use cases
✓ On-premise or European dedicated hosting — you decide
✓ Open-source LLMs: Llama, Mistral, Qwen and specialized models — license-free
✓ Model fine-tuning on your company data and terminology
✓ RAG systems: LLM access to your internal documents and knowledge bases
✓ Custom frontends: chat, search, admin — usable without programming skills
✓ API endpoints for integration into existing software and workflows
✓ Multi-model operation: different models for different tasks on one server
✓ Complete documentation and team onboarding
✓ Ongoing support: monitoring, model updates, performance optimization
✓ Direct contact with the founder — no account manager
FAQ
Frequently asked questions about private LLM servers
What does a private LLM server cost?
That depends on configuration. An IRIDIUM™ Private Server for one to two use cases — including hardware, model deployment, frontend and onboarding — starts at €7,000. Systems with multiple models, RAG integration, fine-tuning and comprehensive frontends typically range from €12,000 to €25,000. For ongoing operation, we offer monthly support packages from €500. In the diagnostic call, you get an exact configuration and binding cost framework.
Isn’t a private server overkill for an SME?
No. IRIDIUM™ systems are deliberately sized for SMEs — not for corporations with data science departments. An entry system is a compact mini PC with a powerful GPU, smaller than a shoebox. No server room, no rack, no IT department as prerequisite. The question isn’t your company size but whether you have use cases that justify a dedicated server. From the point where your team uses AI daily, your own infrastructure pays for itself.
Which models can I run on the server?
Any open-source model suited to your hardware. Currently we most frequently deploy Llama, Mistral, Qwen and specialized embedding models for RAG systems. You’re not locked to one model — multiple models for different tasks can run on the same server. And when a more capable model appears, it gets evaluated and deployed. No contract change, no vendor switch. A model update.
How does quality compare to GPT-4 or Claude?
For most business use cases — text generation, document processing, summarization, knowledge queries, email drafts — current open-source models deliver comparable quality. For highly complex reasoning tasks or peak creative performance, the largest proprietary models still have an edge. In the consultation, we honestly assess whether your use cases run equally well or better on a local model — or whether a hybrid approach makes more sense.
Can I expand the server later?
Yes. The IRIDIUM™ architecture is modular. You can start with one use case and expand step by step — additional models, more users, new data sources, additional frontends. When hardware capacity is reached, you can upgrade or add a second server. You grow with your requirements, not with a vendor’s price list.
Do I need an IT team to operate the server?
Not necessarily. We hand over a documented, tested system that runs stably. If you have internal IT staff, they can take over operations. If not, we offer ongoing support — monitoring, updates, troubleshooting — as a monthly package. You decide whether to operate the server fully in-house, fully outsource or take a hybrid approach.
What happens if the hardware fails?
The same as with any other server in your business. Configuration and RAG data backups are created regularly. In case of a hardware defect, the system is restored on replacement hardware. The models themselves are open source and can be redeployed at any time. As part of our support packages, we handle backup strategy and provide redundancy where needed.
What’s the difference between an IRIDIUM™ Private Server and a self-built Ollama setup?
The technical foundation is similar — but the difference lies in everything around the model. An IRIDIUM™ system comes with production-ready frontends, RAG integration, workflow connections, monitoring, documentation and support. An Ollama terminal on a desktop PC is an experiment. An IRIDIUM™ Private Server is infrastructure your entire team works with productively.
Next step
Find out if a private LLM server
is the right step for your business
15 minutes. Free. Concrete. No obligation.