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
"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
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.
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
Other Articles that may be of interest:
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