NVIDIA DGX Station
The first personal supercomputer for machine learning and artificial intelligencedesigned for your office.
Let’s take a look at the NVIDIA DGX in detail, first from hardware point of view.
|Parameter||NVIDIA DGX-2||NVIDIA DGX-1||NVIDIA DGX Station|
|GPUs||16× NVIDIA Tesla V100 32GB||8× NVIDIA Tesla V100 32GB||4× NVIDIA Tesla V100 32GB|
|Performance||2 PetaFLOPS||1 PetaFLOPS||0,5 PetaFLOPS|
|GPU memory||512 GB in total||256 GB in total||128 GB in total|
|CPU||2× Platinum 8168, 2.7 GHz (24 cores)||2× E5-2698 v4 2.2GHz (20 cores)||E5-2698 v4 2.2GHz (20 cores)|
|NVIDIA CUDA cores||81 920||40 960||20 480|
|NVIDIA Tensor cores||10 240||5 120||2 560|
|GPU interconnect||NVSwitch, non-blocking, 2.4 TB/s||NVLink, hypercube topology||NVLink|
|RAM||1,5 TB||512 GB||256 GB|
|HDD||2× 960GB NVME SSD, 8× 3.84TB NVME SSD||4× 1,92TB SSD||4× 1,92TB SSD|
|Network||2× 10/25Gb Ethernet, 8× 100Gb Infiniband/Ethernet||2× 10Gb Ethernet, 4× 100Gb Infiniband/Ethernet||2× 10GbE|
|Power consumption||10 kW||3 200 W||1 500 W|
|Case||rack, 10U||rack, 3U||tower, watter cooling of GPU, CPU|
What is more interesting, however, is the already mentioned software package offered by NVIDIA machines. All of these offer pre-installed and performance-tuned environments for machine learning (e.g. Caffe, resp. Caffe 2, Theano, TensorFlow, Torch, nebo MXNet) or an intuitive environment for data analysts (NVIDIA Digits). All of this is elegantly packed in Docker Containers. Such a tuned environment provides 30% more power for machine learning applications against applications deployed purely on NVIDIA hardware. The main advantage of the pre-installed environment is the deployment speed, which is in units of hours.
The strength of the NVIDIA solution is to support the entire system. Hardware support (in case of failure of any of the components) is a matter of course. Software support for the entire environment is critical if something does not work. The customer has hundreds of developers ready to help. Support is part of NVIDIA DGX purchase. It is available for 1 or 3 years and can be further extended after this time.
With a combination of tuned hardware, software and NVIDIA support, NVIDIA DGX systems delivers significantly higher performance and acceleration in learning phase:
NVIDIA DGX systems represents huge computing power. When designing an architecture, it is necessary to consider their involvement in the overall IT infrastructure and its tuning to achieve maximum performance. NVIDIA has introduced NVX DGX Reference Architecture, including networking and storage arrays. Here you will find individual design proposals that describe the overall infrastructure solution for running ML and AI applications.