PCs and Workstations for AI, machine learning, and deep learning

More and more AI workloads are migrating from the cloud to on-premises systems: for data protection reasons, to eliminate API costs, and to achieve minimal latency. MIFCOM’s AI workstations are built specifically for this purpose and are divided into four specialized deployment profiles. You can configure each system component by component to suit your workload; they are manufactured and tested in Germany. You can choose between AMD and Intel platforms as a base, while the four profiles differ primarily in the configured graphics card and RAM configuration.

Local LLM AI Workstations: Run Large Language Models Locally

If you want to run large language models like Llama or Mistral directly on your hardware, the Local LLM AI Workstations are your starting point. The graphics memory determines which model sizes run smoothly: if the model fits entirely into the VRAM, the output speed remains high. With up to 96 GB of graphics memory per card, you can run even large models locally, in compliance with data protection regulations and without ongoing API costs, since your data never leaves your network.

Image & Video AI Workstations: Fast Generation for Images and Videos

For Stable Diffusion, ComfyUI, and video generation, short computation times per run are key. The Image & Video AI Workstations deliver the necessary GPU performance so you can test prompts, models, and parameters in rapid succession instead of waiting for renderings.

AI Development Workstations: The Environment for PyTorch and TensorFlow

The AI Development Workstations offer the balance of computing power and memory capacity required for development and fine-tuning. You can refine existing models using methods such as LoRA and QLoRA without needing the resources of a full training system. This bridges the gap between pure inference and training your own models from scratch.

AI Training Workstations: Multi-GPU Performance for Your Own Models

At the top of the lineup are the AI Training Workstations, featuring multi-GPU support for up to several NVIDIA RTX PRO 6000 GPUs, each with 96 GB of graphics memory. This allows you to train your own neural networks from scratch and handle fine-tuning runs that would overwhelm a single graphics card. Multiple linked GPUs share both the computational load and the available graphics memory.

Which AI workstation fits your workflow?

As a rule of thumb: Local LLM for running large language models locally, Image & Video for generative image and video work, Development for development and fine-tuning, and Training for the computationally intensive training of your own models. In the Workstation Configurator, you can customize your system to your specific needs. If you’re still unsure which of the four profiles is right for your project, our AI PC Buying Guide will help you with concrete recommendations. Or you can start by exploring our Workstations and narrow down your options based on application and intended use.