An AI workstation combines the computing power of a professional GPU with the flexibility of a local workstation.
This is particularly useful if you:
Depending on the application, different platforms may be suitable. The best platform for your needs depends on your model, storage requirements, and intended use.
For professional AI, visualization, simulation, and inference workloads.
For cutting-edge AI applications and sophisticated local development.
For compact, local AI development and working with modern AI models right at your desk.
Our AI training workstations are equipped with the latest and most powerful software solutions to optimise your deep learning and AI training:
For local AI development, prototyping, inference, and smaller fine-tuning tasks.
For larger models, more sophisticated fine-tuning, computer vision, simulation, and professional AI development.
For workloads that require multiple GPUs but do not yet require the upgrade to a dedicated server.
Unsere leistungsfähigen Speicherlösungen bieten hohe Kapazität und Geschwindigkeit, um große Datenmengen effizient zu speichern und abzurufen. Perfekt für die Anforderungen von Big Data und maschinellem Lernen.
Unsere AI Training Server sind für maximale Rechenleistung und Flexibilität konzipiert. Sie ermöglichen effizientes Training komplexer AI-Modelle und sind ideal für Forschung und Entwicklung.
Unsere Inference Hardware bietet die nötige Rechenkapazität, um AI-Modelle in Echtzeit auszuführen. Ideal für Anwendungen wie autonome Fahrzeuge, Bildverarbeitung und Sprachsteuerung.
AI training workstations are designed for high and sustained computational workloads. Depending on the configuration, they feature high-performance NVIDIA GPUs, ample GPU memory, appropriate cooling, fast NVMe storage, ECC memory, and advanced networking or management capabilities.
No. Key factors include the number of available PCIe slots, GPU spacing, power supply, cooling, driver and framework compatibility, and the scalability of the training process. Compact unified-memory systems and traditional multi-GPU workstations should therefore be evaluated separately.
GPUs (Graphics Processing Units) are crucial for the training of AI models, as they enable the parallel processing of large amounts of data. This speeds up the training process considerably compared to conventional CPUs.
The choice depends on the specific requirements of your project, including the type of models to be trained, the size of your data sets and your budget. A consultation can help you find the ideal configuration based on your needs.
Yes, AI Training Workstations are versatile and can be used for a variety of computationally intensive tasks, including video editing, 3D modelling and simulations.
Yes. In addition to training and fine-tuning, many systems are also suitable for local inference, computer vision, data analysis, simulation, and development. Which tasks can be meaningfully performed locally depends, among other things, on memory, model size, latency requirements, and software.
Support can range from technical assistance and troubleshooting to regular updates and maintenance services.
That depends on the configuration and the agreed-upon scope of delivery. Options include Ubuntu, NVIDIA drivers, the CUDA Toolkit, cuDNN, PyTorch, TensorFlow/Keras, JAX, and reproducible container environments. Versions and the scope of installation are documented in the proposal.
