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Recommended Systems for NVIDIA DIGITS/Caffe



GPU Workstation


Compact, quiet and cool -- GPU accelerated workstation for Deep Learning workloads in a package small enough to be taken as a carry-on on an airplane.

GPU Workstation


Full "DIGITS" configuration using our highest quality motherboard with up to 4 GPU's at full X16. Up to 512GB of RAM and a wide range of storage options.


Which system is right for you?

Compact GPU Workstation

  • Well utilized for Deep Learning workloads with DIGITS and Caffe
  • Compact, but not "too" small
  • Efficient and quiet cooling under heavy load
  • Optional Airline compliant carrying case

The small size is of this workstation is not necessarily it's most important feature

  • Tested with million image DNN classification jobs
  • 1 or 2 NVIDIA Pascal GPU's for compute
  • Intel Xeon or core i7 CPU (up to 22-core)
  • Up to 256GB mem
  • Recommended hardware configs: ( other options available )
    • 1 or 2 Titan X Pascal or GTX 1070
    • Intel E5 1630v4 4-core 
    • 64 or 128GB mem
    • 1 or 2TB system SSD 

We have tested this system using the NVIDIA DIGITS software stack with Caffe and found it to give very good performance under heavy load.[link coming soon]

"This system always makes me smile when I have it under load"

-- Dr D.B. Kinghorn

DIGITS GPU Workstation

  • Updated version of our  "DIGITS" workstation
  • Best workstation configuration for GPU focused workloads like DNN with Caffe  
  • Can train GoogLeNet on a 1 million ImageNet subset for 30 epocs in 8hr
  • Highest quality motherboard
    • 4 Full X16, PLX switched, metal reinforced PCIe slots
    • converged 10G network
  • Optimal chassis with excellent cooling and quiet operation

Our main platform for GPU accelerated Machine Learning applications 

  • Recommended hardware configs ( other options available )
    • 2 or 4 Titan X Pascal GPU's
    • Intel Xeon E5 1660v4 8-core or core-i7 6950X 10-core
    • 128 or 256GB memory
    • 1TB system SSD, 2TB data SSD, 4GB storage HD

We have trained deep neural networks with complex models and large data sets, utilizing 4 TitanX GPU's with this system.[link coming soon] 

Note: Some workloads may not scale well on multiple GPU's You might consider using 2 GPU's to start with unless you are confident that your particular usage and job characteristics will scale to 4 cards. We can pre-wire for 4 cards for easy expansion.  


Looking for more detail on our testing and why we chose this hardware? Have a look at the testing blog post![link coming soon]

NVIDIA DIGITS, Caffe, and Machine Learning Articles:

If you are configuring a system for workloads like training DNN's with NVIDIA DIGITS and Caffe, we have a number of articles that you may be interested in:

Simple DIGITS install on Ubuntu 14.04

Install Ubuntu 16.04 or 14.04 and CUDA 8 and 7.5 for NVIDIA Pascal GPU
More general multi-version CUDA setup on Ubuntu 14.04 and 16.04 

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