Processing images to form 3D maps and models in Pix4D is a time-consuming task, but our workstations are built specifically to give you maximum performance.
Like most developers, Pix4D lists system requirements that can be used to help ensure the hardware in your system will work with their software. However, most “system requirements” lists tend to cover only the very basics of what hardware is needed to run the software, not what hardware will actually give the best performance. In addition, sometimes these lists can be outdated, list old hardware revisions, or simply show sub-optimal hardware.
Because of this, we created our own benchmark tool for Pix4D and have conducted our own testing here at ProX PC. Based on the results of those tests, we have come up with our own list of recommended hardware – as well as specific workstations tailored with these recommendations in mind.
Processor (CPU)
How does Pix4D utilize the CPU?
Each step within Pix4D makes use of the CPU in a different way. Overall, Pix4D is moderately effective at utilizing multiple CPU cores – with the effectiveness of added cores varying between the different processing steps. Clock speed is important too, though, so the best processor choices balance these two specifications.
Pix4D’s website includes a description of how the CPU (and other components) are utilized in each step, which can be broken down pretty simply:
Step 1 (Initial Processing) benefits greatly from high clock speed, without much regard for core count. In small projects, this step can be 25-45% of the total processing time, making clock speed a big factor in that type of workload – but in larger projects, it may only be 10% or less of the total time.
Step 2 (Point Could and Mesh) utilizes all the cores in a CPU, and while clock speed is still a factor it definitely takes a back seat. This step is often the longest, especially on models, where there is no Step 3, making high core count processors more appealing for those projects.
Step 3 (DSM, Ortho, and Index) is only used when working with maps, and falls between Steps 1 and 2 in terms of how it uses the CPU. Core count is still important, but it seems like the number of cores used effectively is more limited.
What CPU is best for Pix4D?
The type of projects you are running in Pix4D will determine what type of CPU will give you the best results:
Intel Core i9 14900K 24 Core – This is the top-performing processor in our Pix4D model benchmarks, and also does the best with small maps (under about 500 images). AMD Threadripper 7970X 32 Core – This higher core count processor offers the best performance in Pix4D when working with bigger maps, and it supports additional system memory which can help with extremely large projects.
Is the CPU or GPU more important for Pix4D?
Pix4D primarily uses the CPU for calculations but can do some processing on the GPU as well. In particular, the Initial Processing (step 1) and Point Cloud and Mesh (step 2) will benefit from the presence of a compatible video card. The video card is also utilized after processing, when viewing the ray cloud that has been created, and there it is the raw 3D drawing capabilities of the card that matter (rather that CUDA).
Video Card (GPU)
Memory (RAM)
Storage (Drives)
Product
Workstations for Pix4D
Processing images to form 3D maps and models in Pix4D is a time-consuming task, but our workstations are built specifically to give you maximum performance.
Intel Based Workstations
AMD Based Workstations
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.