07.09.2026
Extract and Validate Documents with AI: Self-Hosted Document Processing
Since 1 January 2025 every German company has to be able to receive e-invoices. Part of the incoming documents now arrive as structured XML and...
Hosting Large Language Models (LLM) in Germany - secure, high-performance and ready for operation. Privacy-focused with dedicated GPU infrastructure.
Companies worldwide trust WZ-IT
This page is the operations layer of our AI offering: it carries the solutions you buy - from the offer finder to the internal assistant. The stack stays in your ownership throughout.
Large Language Models (LLM) are AI models that can understand and generate natural language. For companies, they offer enormous opportunities: from automating customer communication and intelligent document analysis to coding assistants and knowledge management.
With our LLM Hosting Germany, you can operate these powerful models in your own privacy-focused infrastructure - without having to pass on your sensitive data to global cloud providers.
Whether Llama 4, Qwen3, DeepSeek-R1 or other open models - we handle installation, operations and optimisation of your LLM infrastructure.
Full control over your AI infrastructure
German locations can be selected for models, storage and processing. External integrations, remote access and subprocessors are documented so that the specific privacy assessment remains traceable.
You determine the models, interfaces and data flows. Unneeded external model APIs can be omitted; enabled integrations and outbound connections are documented transparently.
Meet strict compliance requirements in regulated industries such as healthcare, finance or public administration.
No hidden API costs, no surprises when it comes to billing. Predictable monthly fixed costs instead of pay-per-token with cloud providers.
Dedicated GPU resources without sharing. Optimal latency for your applications without dependence on global cloud services.
Fine-tuning and customization of your models to your specific requirements. No restrictions from API limits or vendor lock-ins.
What is needed for professional LLM hosting?
Hosting Large Language Models places special demands on the infrastructure. We ensure that everything is optimally configured.
LLMs need GPUs with enough VRAM. Rule of thumb for the weights: quantized (Q4) roughly half a gigabyte per billion parameters, in FP16 about four times that. Context and KV cache add on top depending on context length and concurrency - so we plan around 48 GB for Llama 3.1 70B and around 20 GB for Gemma 3 27B.
In addition to GPU memory, you need sufficient RAM (at least 64 GB) and fast NVMe storage for model files and caching. The model files themselves take roughly 15 GB (7B) to 150 GB (70B) on disk in FP16; quantized operation needs correspondingly less.
For production applications with multiple users, a stable, fast network connection is essential. Our servers offer gigabit connectivity with low latency within Germany.
The complete software stack including CUDA drivers, Ollama, OpenWebUI and container orchestration is installed, configured and kept up-to-date by us.
Professional monitoring of GPU utilization, temperature monitoring and security updates are part of the service. Automated backups are available as an option.
As requirements grow, we scale your infrastructure horizontally (multiple servers) or vertically (more powerful GPUs). Load balancing between multiple instances is possible.
We take care of everything - you simply use your LLMs
From initial setup to daily operations: Our Managed LLM Hosting handles all technical aspects.
On request: installation of the models you want (Llama, Qwen, Gemma, Mistral, DeepSeek, GPT-OSS), Ollama or vLLM as the model server, OpenWebUI as the web interface and optional API endpoints for your applications.
We monitor your LLM infrastructure around the clock, perform system updates and optimize GPU performance. How quickly we respond to an alert depends on the agreed service level.
Our team supports you with model selection, integration into your applications and optimization for your specific use cases. Priority support via email and optionally phone/video.
Fixed monthly costs without hidden fees, no pay-per-token billing. Monthly cancellation. We agree the concrete price based on the configuration in the initial call.
A selection of popular open-source LLMs

Meta's current generation as a mixture of experts: 109B parameters with 17B active per token, natively multimodal and a very long context. Runs on a single 96 GB card.

Google's most powerful model that runs on a single consumer GPU. Vision support for image analysis integrated.

Open reasoning model that exposes its chain of thought and supports tool calls.
European model from Mistral AI with a clear licence position - 24B dense, fits a 20 GB card and the obvious choice when model provenance matters.
Microsoft's compact 14B model with outstanding performance for its size. Optimal for resource-efficient deployments.
Alibaba's multilingual family under Apache 2.0, from 0.6B to 235B. A strong all-rounder with good coding and reasoning behaviour - the most common choice for local operation.

The easiest way to run LLMs locally. Perfect for development, prototyping, and small to medium production workloads.

High-performance inference engine for production workloads. Optimized for maximum throughput and minimal latency under high load.
Which framework is right for you?
We recommend Ollama for simple use cases, development and moderate load. For production applications with many concurrent users and high performance requirements, vLLM is the better choice. We're happy to help you choose!
Typical use cases and target groups
Offer your clients AI-powered services like content creation, SEO analysis or chatbots - with your own LLM infrastructure instead of expensive OpenAI API costs.
Universities, research institutions and educational organizations use their own LLMs for scientific work, studies and teaching without data privacy concerns.
SMEs in Germany use LLMs for internal knowledge bases, customer service automation, code analysis or document classification.
Connect your LLM with your own documents, wikis or databases. Employees ask questions in natural language and receive precise answers from your knowledge base.
Development teams use LLMs locally for code completion, review and documentation - without sending source code to external APIs.
Create product descriptions, marketing texts or social media content with your own LLM in your corporate language.
Analyze and classify large volumes of documents, contracts or emails automatically and privacy-focused.
With RAG, you connect your LLM with external knowledge sources. The model searches your documents and generates answers based on actual facts from your database. Ideal for company wikis, support databases or research archives.
Enquiry
Describe models, access, and data sources. We derive the infrastructure and operational scope.
Why self-hosting is often the better choice
| Feature | Self-Hosted (WZ-IT) | Cloud APIs (OpenAI, etc.) |
|---|---|---|
| Data Privacy | Hosted in Germany, privacy-focused | Data goes to US providers |
| Costs | Fixed monthly costs | Variable token prices, often more expensive |
| Control | Full control over models | Dependency on provider |
| Customization | Fine-tuning possible anytime | Limited or expensive |
| Latency | Optimal (Germany) | Variable, depends on region |
| Availability | Dedicated resources, no rate limits | Rate limits, outages possible |
Everything you need to know about LLM hosting
Yes, you can use any open-source model that is compatible with Ollama or vLLM. This includes all common models like Llama, Gemma, Mistral, DeepSeek, Phi, and many more. We also support you with fine-tuning or converting your own models.
Absolutely. You can upgrade to a more powerful GPU at any time (e.g. from RTX 4000 to RTX 6000) or add additional servers for load balancing. We offer both vertical and horizontal scaling.
Every model that runs with Ollama or vLLM: Llama 4 (Scout, Maverick) and still Llama 3.3, Qwen3 (0.6B-235B), Gemma 3 (1B-27B), DeepSeek-R1, Mistral Small 3.2, Phi-4, GPT-OSS (20B, 120B) and hundreds more. The full list is at ollama.com/library. As of August 2026.
With Ollama on our RTX 4000 you can achieve about 10-15 tokens/second with Gemma 3 27B - an experience value, not a guaranteed performance level. With vLLM, significantly higher throughput is possible under high load; in its own benchmarks the vLLM project measures up to 24x more throughput compared to HuggingFace Transformers. Exact performance depends on the model, context length and the number of simultaneous requests.
Yes, both Ollama and vLLM provide OpenAI-compatible APIs. You can easily switch your applications by changing the API URL - the code remains almost identical. Additionally, there are native client libraries for Python, JavaScript, Go and more languages.
Our monitoring usually detects problems before they have an impact. In case of disruptions, we respond immediately. As a managed service customer, you have priority support via email, with optional phone/video support available.
Yes, depending on the available VRAM you can load multiple models in parallel or switch between them. Ollama manages this automatically. On the Pro configuration with 96 GB VRAM you can, for example, keep a 27B and a 7B model loaded at the same time.
We keep the system (OS, CUDA, Ollama/vLLM) up-to-date and install security updates promptly. Model updates are done on request - you decide when you want to use a new model release.
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No risk: worst case, you leave with a clearer understanding of your project than before.


“WZ-IT's advice on our Azure migration was technically sound and completely non-binding right from the intro call - we took away a great deal.”
From local AI integration to architecture, data sovereignty and ongoing operations.
“WZ-IT moved our studio infrastructure from decentralised individual devices to a central platform: every site is securely connected via VPN, new devices are onboarded automatically and an entire site is provisioned from a template, without manual steps on location. What impressed me most is the breadth and depth of their knowledge: Timo and Robin are not a typical IT provider who sets up a server and leaves. The two of them think their way into highly complex infrastructure and software topics, work through every requirement we put in front of them, and build networking, provisioning and operations so that everything fits together in the end. WZ-IT is an excellent partner for complex software, network and architecture projects.”

Steve Kirchner
Managing Director, nextGYM GmbH

Whether a specific IT challenge or just an idea - we look forward to the exchange. In a brief conversation, we'll evaluate together if and how your project fits with WZ-IT.