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Recommended Systems for Machine Learning / AI TensorFlow etc..



Compact ML/AI
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.

"Machine Learning"
GPU Workstation


Full "ML/AI" configuration using our highest quality motherboard with up to 4 GPU's at full X16. Up to 256GB of RAM and a wide range of storage options.


Which system is right for you?

Compact AI/ML GPU Workstation

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

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

  • Tested with million image DNN classification jobs
  • 1 or 2 NVIDIA Pascal or Volta GPU's for compute
  • Intel  core i7 or i9 CPU 
  • Up to 64GB mem
  • Recommended hardware configs: ( other options available )
    • 1 or 2 Titan V or Titan Xp, GTX 1080ti or GTX 1070 Pascal GPU's
    • Intel Core i9 10-core 
    • 64 mem
    • 1 or 2TB system SSD 

We have tested this system using the NVIDIA NGC software stack with TensorFlow and found it to give very good performance under heavy load.

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

-- Dr D.B. Kinghorn

 Machine Learning / AI GPU Workstation

  • Updated version of our  "DIGITS" workstation
  • Best workstation configuration for GPU focused workloads like DNN with TensorFlow 
  • Can train GoogLeNet on a 1 million ImageNet subset for 30 epocs in under 8hr
  • Highest quality motherboard
    • 4 Full X16, PLX switched, metal reinforced PCIe slots
  • 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 V, Xp, or GTX 1080Ti  GPU's
    • Intel Xeon-W 2145 8-core or Xeon-W 2195 18-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 Titan-V GPU's with this system.

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.  If you are using Tensorflow multi-GPU scaling is generally very good.


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

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:

Multi-GPU scaling with Titan V and TensorFlow on a 4 GPU Workstation


TensorFlow benchmarks on 4 NVIDIA Titan V GPU's. 

How-To Setup NVIDIA Docker and NGC Registry on your Workstation - Part 1 Introduction and Base System Setup​

The first in a series of 5 posts about using the NVIDIA NGC Docker Registry on your Workstation

The Best Way To Install Ubuntu 16.04 with NVIDIA Drivers and CUDA
A simple and "script-able" method for installing Ubuntu 16.04 and CUDA 

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