Dedicated GPU power for AI model training and inference. Highest performance from Germany with complete data protection.
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GPU servers for AI are specialized high-performance servers equipped with NVIDIA RTX Professional GPUs. Unlike conventional servers, they use the parallel computing architecture of graphics processors to train and execute AI models up to 100x faster. Especially for deep learning, neural networks, and large language models, GPUs are indispensable as they can perform thousands of calculations simultaneously.
Hosted in Germany means full GDPR compliance, low latency, and maximum data sovereignty. Your training data and models never leave German jurisdiction – a critical advantage for companies with sensitive data.
Our GPU servers are offered as a managed service: We handle installation, GPU driver optimization, monitoring, and maintenance, while you focus on your AI projects.
The decisive performance advantage
CPUs are optimized for serial computations and typically have 8-64 cores. GPUs, however, have thousands of cores (e.g., in the RTX 6000 Blackwell Max-Q) specifically designed for parallel matrix operations – exactly what deep learning requires.
Modern NVIDIA RTX GPUs feature special Tensor Cores optimized for AI computations. These achieve up to 1457 TFLOPS for FP16 calculations (Mixed Precision Training) – computational power impossible with CPUs.
Training requires GPUs with high VRAM (96GB) to process large models and batches. Inference (production deployment) is about low latency and high throughput – GPUs excel with response times in the millisecond range.
A Llama 70B model that takes 30+ seconds per response on a CPU delivers results in under 2 seconds on an RTX 6000 Blackwell Max-Q. For training workloads, the difference can be even more dramatic: hours instead of days.
Latest generation NVIDIA RTX Professional GPUs
The perfect solution for inference and small to medium-sized models
20 GB GDDR6 VRAM
Sufficient for models up to 13B parameters (quantized) or 7B parameters (FP16)
306.8 TFLOPS (FP16)
Outstanding performance for fast inference in production environments
6,144 CUDA Cores
Ada Lovelace architecture with 3rd Gen RT Cores and 4th Gen Tensor Cores
Ideal for:
Ideal for: Chatbots, code assistants, RAG systems, real-time inference
High-end performance for AI model training and large models
96 GB GDDR7 VRAM
For models up to 70B+ parameters (FP16) or 100B+ parameters (quantized)
Flagship Performance
Professional computational power for demanding training workloads
Blackwell Architecture
Flagship Blackwell GPU with maximum parallel processing power
Ideal for:
Ideal for: Fine-tuning, transfer learning, large language models, multi-modal AI
All servers are hosted in DC ISO 27001-certified data centers in Germany. This guarantees GDPR compliance, low latency (<10ms to German cities), and complete data sovereignty. Your AI training data stays in Germany.
We offer both leading open-source frameworks for LLM inference
The beginner-friendly solution for local LLM hosting. Ollama makes it extremely easy to deploy models such as Llama, Gemma or Mistral with a single command. Perfect for rapid prototyping and smaller projects.
Ideal for:
Prototyping, small to medium workloads, simple setup, developer-friendly
The high-performance solution for productive inference workloads. vLLM uses PagedAttention and continuous batching for up to 24 times higher throughput than Ollama. Ideal for applications with high traffic and strict latency requirements.
Ideal for:
Production-ready apps, high-traffic systems, API services, maximum performance
Ollama is ideal for development, prototyping and smaller deployments (up to approx. 50 requests/min). vLLM is the choice for productive high-performance scenarios with hundreds of simultaneous requests. We can run both frameworks in parallel on one server or recommend the right one depending on your use case.
| GPU Model | VRAM | TFLOPS (FP16) | CUDA Cores | Primary Use Case | From Price/Month |
|---|---|---|---|---|---|
| RTX 4000 SFF Ada | 20 GB | 306.8 | 6.144 | Inference, models up to 13B | 499€ |
| RTX 6000 Blackwell Max-Q | 96 GB | – | – | Training, models up to 70B | On Request |
All prices are monthly, with no hidden costs
RTX 4000 SFF Ada for inference workloads
RTX 6000 Blackwell Max-Q for training & large models
No setup fees
Scaling possible at any time
DC ISO 27001 certified
Server location Germany
How our customers use GPU servers
A digital agency hosts Llama 70B and Gemma 27B for multiple enterprise clients. The models are used for customer-specific chatbots and content generation. Result: 90% cost savings compared to OpenAI API with full data control. Response time under 2 seconds.
A research institute uses RTX 6000 Blackwell Max-Q for fine-tuning Llama models on German medical datasets. Training that would take weeks on CPUs is completed in 2-3 days. GDPR compliance is guaranteed for sensitive health data.
A mid-sized software company integrates a RAG system (Retrieval-Augmented Generation) into their ERP software. With DeepSeek R1 on RTX 4000 Ada, customer inquiries are intelligently answered – fully on-premise and GDPR-compliant. ROI achieved after 4 months.
An AI startup develops a code review assistant. The prototype runs on GPU Server Basic with Gemma 27B. Cost: €499/month instead of €5,000+ with cloud providers. After product-market fit, upgrade to Pro model for multi-model deployment.
At an average of 1 million tokens per day, OpenAI GPT-4 costs approximately €15,000/month. With your own GPU server: €1,399/month + one-time implementation. Break-even after 2-3 months, then pure cost savings with full data control.
Free consultation and technical feasibility analysis
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Timo Wevelsiep & Robin Zins
CEOs of WZ-IT
