MultiGPU workstation for CAD, 3D rendering and video editing workflows

Multi GPU refers to the use of more than one graphics card (GPU) in a single computer system to increase computing power for graphically intensive applications. This can be done through special hardware configurations such as NVIDIA SLI (Scalable Link Interface) or AMD CrossFire, which allow multiple GPUs to be interconnected. Even if there is no special hardware connection, modern operating systems and software solutions can support multiple GPUs in the same system.

Discover the advantages of multi GPU systems and increase your computing power for demanding applications such as 3D rendering, simulations or video editing. Find out more about the optimal areas of application and configurations for Multi GPU or get advice from Uli Ludwig.

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More performance thanks to Multi GPU?

How does Multi GPU work?

  • parallel processing: By using multiple GPUs, computationally intensive tasks can be split up and processed in parallel. Each GPU can take on a subtask, which significantly reduces the overall computing time.

  • Increased VRAM capacity: Some applications can utilise the graphics memory (VRAM) of all GPUs used together. This makes it possible to process larger amounts of data simultaneously.

  • Load distribution: With MultiGPU configurations, the load that an application places on the system can be distributed across several GPUs. This leads to more efficient utilisation of the hardware and can increase performance.

The use of MultiGPU systems is particularly useful in areas where high computing power is required, especially for tasks such as:


  • 3D rendering: complex 3D scenes with a high level of detail and many effects benefit from the additional computing power provided by multiple GPUs.

  • Simulations: Physics or particle simulations that need to be executed in real time can benefit from parallel processing.

  • Machine learning and AI: Processing large amounts of data and training models is typically computationally intensive, so multiple GPUs can significantly speed up calculations.

  • 4K and 8K video editing: These high-resolution formats require enormous computing power, especially when complex effects or colour corrections are applied.

Some programs either offer direct support for Multi GPU or benefit greatly from it, especially in rendering, simulations or real-time visualisations. However, the actual increase in performance depends on the specific application and the workflows used. The following list does not claim to be exhaustive. On request, we will be happy to clarify whether your programme will also benefit from a Multi GPU configuration:

CAD programmes

  • Autodesk AutoCAD: AutoCAD itself does not generally utilise MultiGPU systems as it mainly relies on CPU power. However, more complex CAD applications such as Autodesk 3ds Max or Autodesk Maya benefit greatly from MultiGPU systems, especially for rendering and simulations.

  • SolidWorks: SolidWorks does not use MultiGPU directly, as the application is heavily CPU-dependent. Rendering tools such as SOLIDWORKS Visualize, on the other hand, can be accelerated by MultiGPU configurations.


Video editing software

  • Adobe Premiere Pro: Supports the use of multiple GPUs for rendering and effects playback. However, performance does not increase linearly with the number of GPUs, as efficiency depends on the implementation and specific effects.

  • DaVinci Resolve: This software is particularly well optimised for multi-GPU systems, especially in the Studio version. The software can use multiple GPUs to increase performance in colour grading and video editing.

  • Blender: Blender, an open-source 3D modelling and rendering software, also supports multi-GPU configurations, especially when rendering with Cycles.

The use of MultiGPU makes sense in many professional areas, especially when it comes to computing-intensive tasks such as 3D rendering, simulations and video editing. In the CAD and video industries, MultiGPU systems can offer significant performance gains, especially for applications optimised for parallel processing. However, it is important to consider the specific requirements and software compatibility to maximise the benefits of a MultiGPU configuration:


  • Scalability: Not all applications scale perfectly with the number of GPUs. The performance increase is often not linear, and some programmes benefit more from additional GPU power than others.

  • Software support: Some programs support MultiGPU natively, while others may require additional plugins or special configurations to utilise multiple GPUs effectively.

  • Power consumption and cooling: Multiple GPUs require more power and generate more heat, which requires more cooling and a powerful power supply.

  • Cost: The cost of building and maintaining a multi-GPU system can be significant and must be weighed against the benefits.