Pix4Dengine Hardware recommendations


  • Ubuntu 18.04 64 bit or Windows server 64 bits over Windows 10.
  • CPU quad-core or hexa-core Intel i7/Intel i9/Threadripper/Xeon/.
  • GeForce GTX 1070 and up (compatible with OpenGL 3.2).
  • Storage: SSD.
  • Small projects (under 100 images at 14 MP): 8 GB RAM, 15 GB SSD Free Space.
  • Medium projects (between 100 and 500 images at 14 MP): 16GB RAM, 30 GB SSD Free Space.
  • Large projects (over 500 images at 14 MP): 32 GB RAM, 60 GB SSD Free Space.
  • Very Large projects (over 2000 images at 14 MP): 64 GB RAM, 120 GB SSD Free Space.
  • Swap memory to be set to the generally recommended values based on the RAM size and the OS type.

High-end Build Components

For applications where low processing times and large datasets require the use of high-end workstations, there are high-end components that have proven to work better than others in most cases. An ideal high-end workstation provides balanced resources to avoid bottlenecks that slow the system down overall. Below are some of the components which have performed well with Pix4D.


  • CPU: Threadripper 1950x - Core i9 9900K, Core i7 7980XE.
  • GPU:  GeForce GTX 1070, 1080 Ti.
  • Storage: SSD. 
  • The graphics card may have an improvement on the processing speed for step 1 if the graphics card is compatible with CUDA (NVIDIA Graphic Cards). Processing time of step 2 and step 3 are not affected by the GPU. The GPU also helps considerably with rayCloud visualization. For more information about the use of the GPU: Use of the GPU in Pix4Dmapper.
  • For more information about hardware components usage when processing with Pix4Dmapper: Hardware components usage when processing with Pix4D.
  • For more information about processing speed: Processing speed.
  • Unusually long times have been observed when processing with some Quadro GPU. This is related to some settings in the NVIDIA control panel. For more information: Long Processing Time for Step 3 with Quadro GPUs
  • GeForce is not supported on Windows server platforms. Professional server solutions must
    be selected instead. Keep the GPU drivers always up to date.
  • Using multiple GPUs for processing is not supported in the current version.

Hardware case study

We have performed an experiment to investigate the total processing time using a dedicated machine for Pix4Dengine SDK processing:

  • Python 3.6.7, 64-bit.
  • Ubuntu 18.04.3 LTS 64-bit.
  • CPU: Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz (6 core / 12 logical).
  • RAM - 64GB.
  • Storage: local SSD (no LAN/WLAN transfers).


Inputs Pipeline configuration Outputs Time

2200 images (SONY DSC-RX1R_35.0_6000x4000) 

Agriculture area - large uniform fields


Full (all 3 steps)

Additional configuration:

Densified point cloud (.las)

Digital surface model (.tiff)

Orthomosaic (.tiff)

Calibration: 2 h

Total: 18 h


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