GPU Systems for AI
AI GPU servers and workstations for local AI applications, custom language models and deep learning – from development and testing to production operation on your own hardware.
Our on-prem AI systems provide a preconfigured platform for running modern AI models locally on your own hardware. This enables language models such as Qwen, Llama, Mistral or DeepSeek to be used directly within the company – for example for document search, knowledge management, research, internal assistance systems and the use of in-house expertise.
The choice of AI model remains flexible. Models can be selected, added or replaced depending on the use case. Custom applications, interfaces and company-specific workflows can also be built on top of the platform. In addition, the AI can be extended with internal documents, guidelines and knowledge resources to make company-specific terminology, relationships and processes more accessible.
Ideal for businesses that want to operate AI technology locally, securely and reliably under their own control. No cloud connection. No recurring subscription costs. Your data stays within the company.
Local AI Server for Businesses
Our AI GPU systems can optionally be prepared with CADnetwork® Ready-to-Brain™ technology. The system is configured with NVIDIA drivers, CUDA, cuDNN, Docker and common deep learning frameworks such as TensorFlow and PyTorch. This creates a reproducible working environment for development, testing, training, fine-tuning and inference on your own GPU hardware.
The screenshot shows a typical preconfigured developer environment: Docker containers, GPU detection, framework startup and NVIDIA system status are set up and can be checked directly. This significantly reduces the effort required for drivers, CUDA dependencies, container setup and initial functionality tests.
The container-based architecture makes it easier to keep different framework versions, libraries and project-specific dependencies cleanly separated. Custom containers, applications and workflows can be added without unnecessarily mixing them into the base installation.
This keeps the system flexible: for local development, testing, training, fine-tuning, inference and production deep learning workflows. CADnetwork provides not only the hardware, but a technically coordinated platform consisting of the GPU system, drivers, runtime environment and an optionally preconfigured software stack.
CADnetwork Deep Learning Stack