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Recommended Systems for Machine Learning / AI TensorFlow


Configure a Machine Learning / AI Workstation

Compact Workstation

A compact GPU accelerated workstation for Deep Learning workloads in a package small enough to be taken as a carry-on on an airplane.

Quad GPU Workstation

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

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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 Turing 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 RTX 2080Ti, RTX 2070 or Titan V GPU's
    • Intel Core i9 with AVX512 
    • 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's with TensorFlow or PyTorch 
  • 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 RTX 2080Ti, RTX 2070, or Titan V  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: