Buy AI Development Workstations – CUDA, PyTorch & TensorFlow

With an AI Development Workstation from MIFCOM, you can develop AI applications and adapt existing models to your data—locally, reproducibly, and without cloud costs per experiment. The systems offer the right balance of GPU performance, graphics memory, and system scalability required for development and fine-tuning. You configure each system component by component to suit your workload; they are manufactured and tested in Germany.

Fine-tuning requires extra capacity: our recommendations

Fine-tuning places higher demands on graphics memory than pure inference, since, in addition to the model, gradients and optimizer states must also be accommodated. Methods such as LoRA and QLoRA significantly reduce this requirement and make fine-tuning large models on a single GPU feasible. Our recommendations are based on this:

Our recommendations for AI development workstations:

Your development environment, ready to go

These systems form the foundation for the entire deep learning stack: PyTorch and TensorFlow with CUDA acceleration, Jupyter environments for experiments, and container workflows for reproducible training runs. Plenty of RAM and fast NVMe SSDs keep datasets and checkpoints readily accessible so the GPU doesn’t have to wait for data.

When Fine-Tuning Becomes Training

If your project goes beyond fine-tuning and you want to train your own networks from scratch, our AI Training Workstations lead the way with multi-GPU performance.

Which AI Development Workstation Is Right for You?

Choose your system based on the largest model class you plan to fine-tune regularly, and factor in extra graphics memory for growing datasets. Use the Workstation Configurator to tailor your system to your specific needs. Or start with our AI workstations and compare the four usage profiles. Our AI PC buying guide explains in detail which hardware you need.