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
Deep Learning & Machine Learning
Modern deep learning workloads place different demands on hardware than traditional server applications. When training neural networks, working with transformer models, computer vision or generative AI, large amounts of parallel computations are performed on the GPU. What matters is not only GPU performance, but also available GPU memory, system RAM, storage and the right software environment.
When training neural networks, large amounts of data are repeatedly processed through the model. Powerful NVIDIA GPUs significantly accelerate these parallel computations. For larger models or extensive training datasets, multiple GPUs can be used within a single system.
TensorFlow can use NVIDIA GPUs via CUDA and cuDNN for compute-intensive machine learning and deep learning operations. Our GPU systems can be used as prepared platforms for TensorFlow development, training, testing and production inference.
PyTorch is widely used for deep learning, transformer models, computer vision, research and generative AI. CUDA-capable NVIDIA GPUs enable acceleration of training and inference as well as the implementation of multi-GPU workloads.
Not every AI project requires full model training. GPU systems can also be used for fine-tuning existing models and for subsequent inference. Depending on the application, VRAM, computing performance, throughput or low latency may be the primary consideration.
GPU acceleration is not limited to Large Language Models. Typical applications range from image classification, object detection and segmentation to scientific machine learning models and custom CUDA, TensorFlow and PyTorch applications. The hardware can be configured to match the framework, dataset, model size and required computing performance.
On request, the systems can be supplied with a prepared deep learning environment. NVIDIA drivers, CUDA, cuDNN, Docker and NVIDIA Container Toolkit provide the GPU foundation. TensorFlow, PyTorch and project-specific frameworks can be operated in separate container environments.
GPU Selection and VRAM
Which GPU is suitable for a deep learning server depends heavily on the specific workload. In addition to raw GPU computing performance, the amount of available GPU memory plays an important role. Models, training data, tensors and intermediate results must be processed in VRAM during computation.
GPUs with 16 GB of VRAM are suitable, for example, for development, many machine learning tasks and smaller deep learning models. 32 GB already provides significantly more headroom for demanding training, inference and AI workloads. 48 GB, 72 GB, 96 GB or more GPU memory becomes increasingly relevant as models, batch sizes, resolutions or memory requirements grow.
Important for multi-GPU systems: The memory of multiple GPUs is not automatically combined into one large VRAM pool. For a model or training job to use multiple GPUs, the software must distribute the workload accordingly. Frameworks such as TensorFlow and PyTorch provide different approaches for Data Parallelism and Model Parallelism.
In addition to the GPU, CPU performance, system RAM, NVMe storage and network bandwidth also influence the overall performance of an AI server. Especially with large datasets, parallel preprocessing or multiple GPUs, the platform must be able to provide data fast enough to prevent the GPUs from being bottlenecked by other components.
Hardware Sizing
A powerful deep learning server consists of more than fast GPUs. CPU, system memory, storage, PCIe connectivity and networking must match the respective AI workload and reliably supply the GPUs with data.
The GPU performs most of the parallel computation. At the same time, the available VRAM limits which models, batch sizes and amounts of data can be processed directly on the GPU.
Data preparation, data loaders, compilation and other CPU-side tasks still run on the processor. Depending on the workflow, strong CPU performance can therefore also be crucial for keeping the GPUs fully utilized.
Large datasets, parallel processes and data preparation require sufficient system memory. The required RAM capacity therefore does not depend solely on the amount of GPU memory installed.
Fast NVMe SSDs reduce waiting times when loading large datasets, models and checkpoints. For data-intensive workloads, storage performance can directly affect overall throughput.
When training data is provided by NAS, storage or server systems, network bandwidth also becomes relevant. Depending on the platform, the GPU systems can be equipped with high-speed Ethernet interfaces for centralized storage, team and data center environments.