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Custom Built Workstations for Redshift

By leveraging the incredible computing performance and efficiency of the GPU, Redshift dramatically speeds up rendering, resulting in faster iteration and lower costs. Our Custom built workstations are optimized to provide lots of GPU horsepower for your budget!
Custom Built Workstations for Redshift
Hardware Utilization

Redshift System Requirements and Benchmarks

Like most applications, the system requirements for Redshift can be found on their official website. They also have a good FAQ, which primarily focuses on GPU compatibility and performance. Since their information is split into two sections like that, and since it may tend a bit toward the minimum requirements rather than best specs, we have put together our own hardware recommendations below.

Is Redshift a CPU or GPU based rendering engine?

Redshift is a fully GPU-based rendering engine. This means that the video cards (or GPUs) in your system are what impacts how long renders take to complete, rather than the CPU.

Processor (CPU)

What type of CPU does Redshift need?

In Redshift, as well as most other GPU-based engines, the CPU does not play a direct role in the process of rendering scenes. It does have a small impact on the time spent loading a scene, but that is pretty minimal in the grand scheme of things. However, if you are also using the same system for modeling or animation – in a program like Cinema4D, Maya, or 3ds Max – then you will want a CPU with a high clock speed in order to ensure good performance in those applications. On the other hand, if you also use CPU based rendering engines then having a higher core count would benefit those programs… but it will not improve Redshift’s rendering speed.

Aside from raw performance, it is also important to consider the number of PCI-Express lanes a CPU supports. This will govern how many video cards can be used, which has a big impact since video cards are the primary driver for rendering performance in Redshift.

What is the best CPU for Redshift?

As mentioned above, the CPU itself isn’t going to directly contribute to Redshift’s rendering – but it will determine how many video cards the system can support, which does impact performance. Here are a couple of our recommendations:

AMD Ryzen 7 7700X 8 Core – This is one of the highest clock speed CPUs available, and does very well with both Redshift and modeling / animation applications. It has enough PCI-Express lanes to support dual video cards, along with other standard hardware devices (NVMe drives, etc).


AMD Threadripper PRO 7965WX 24 Core – AMD’s Threadripper PRO processors have far more PCI-Express lanes than more consumer-oriented models. Combined with the right motherboard, this CPU can allow up to four video cards in a single tower workstation. The trade-off is that the platform as a whole is more expensive, but being able to fit three or four high-end GPUs in a single system is worthwhile in many workflows.

Will a more powerful CPU help at all in Redshift?

A faster CPU will allow you to extract mesh data, load textures, and prepare scene data more quickly, but it will make no impact on how long it takes each individual render to complete. We recommend going for a high clock speed CPU without worrying about core count – and without spending too much money there, instead focusing your budget on more powerful GPUs (and/or more of them).

Video Card (GPU)

Memory (RAM)

Storage (Hard Drives)

Frequently Asked Questions

ProX PC's Maven workstations stand out for their exceptional performance and versatility. They feature high-end components, providing powerful computing capabilities for demanding tasks.

Absolutely! Over the years we have helped quite a few of our customers make the move from Mac to PC with customized migration assistance.

Yes, we offer customizable solutions tailored to your specific requirements. Whether you need extra GPU power, additional RAM, or specialized storage solutions.

Yes, our workstations are designed with upgradeability in mind. You can easily upgrade components such as GPU, RAM, and storage over time.
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