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.
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?
- 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 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 Xp, GTX 1080ti or GTX 1070 Pascal GPU's
- Intel E5 1630v4 4-core
- 64 or 128GB mem
- 1 or 2TB system SSD
"This system always makes me smile when I have it under load"
-- Dr D.B. Kinghorn
- 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 Xp or GTX 1080ti 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
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!
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:
NVIDIA DIGITS with Caffe - Performance on Pascal GPUs
Main DIGITS/Caffe hardware testing article
NVIDIA DIGITS Install
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
Other Articles that may be of interest:
- Docker and NVIDIA-docker on your workstation: Integration with your Desktop
- NVIDIA Titan GPUs (3 generations) - CUDA 8 rc performance on Ubuntu 16.04
- What is Machine Learning
- Install Intel Python using conda from Anaconda Python
Why Choose Puget Systems?
We do not add a part to our product line unless we feel we can stand behind it. You can feel confident that any selection you make on our website is a quality product.
By keeping inventory of our most popular parts, and maintaining a short supply line to parts we need, we are able to offer an industry leading ship time of 7-10 business days on nearly all our system orders.
We make sure our representatives are as accessible as possible, by phone and email. At Puget Systems, you can actually talk to a real person!
Click here for even more reasons!