NVIDIA DGX Station
The first personal supercomputer for machine learning and artificial intelligence designed for your office.
Let’s take a look at the NVIDIA DGX in detail, first from hardware point of view.
|Parameter||NVIDIA DGX-A100||NVIDIA DGX Station|
|GPUs||8x NVIDIA Ampere A100 Tensor|
|4× NVIDIA Tesla V100 32GB|
|Performance||5 petaFLOPS AI|
10 petaOPS INT8
|GPU memory||320 GB in total||128 GB in total|
|CPU||Dual AMD Rome 7742,|
128 cores total, 2.25 GHz
(base), 3.4 GHz (max boost)
|E5-2698 v4 2.2GHz (20 cores)|
|NVIDIA CUDA cores||55 296||20 480|
|NVIDIA Tensor cores||3 456||2 560|
|Multi-instance GPU||56 instances||4 instances|
|GPU interconnect||6x NVIDIA NVSwitch 3, non-blocking, 4.8 TB/s||NVLink|
|RAM||1 TB||256 GB|
|HDD||OS: 2x 1.92TB M.2 NVME drives|
Internal Storage: 15TB
(4x 3.84TB) U.2 NVME drives
|4× 1,92TB SSD|
|Network||8x Single-Port Mellanox|
200Gb/s HDR InfiniBand
1x Dual-Port Mellanox
|Power consumption||6 500 W||1 500 W|
|Case||rack, 6U||tower, watter cooling of GPU, CPU|
What is more interesting, however, is the already mentioned software package offered by NVIDIA DGX machines. All of these offer pre-installed and performance-tuned environments for machine learning (e.g. Caffe, resp. Caffe 2, Theano, TensorFlow, PyTorch, 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 base DGX system image contains Ubuntu operating system, NVIDIA GPU drivers and Docker environment for application containers downloadable from NVIDIA GPU Cloudu (NGC). NVIDIA also supports to run these Docker images in Singularity environment.
NVIDIA GPU Cloud
NVIDIA GPU Cloud (NGC) represents repository of the most used frameworks for machine learning and deep learning applications, HPC applications, or NVIDIA GPU cards accelerated visualization. Deploying these applications is a question minutes — copying a link of the appropriate Docker image from NGC repositry, moving it on the DGX system, and downloading and running the Docker container. Content of Docker images — versions of all the libraries and frameworks or setting environment parameters — is updated and optimized by NVIDIA specialists for deployment on DGX systems. https://ngc.nvidia.com/
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, 3 or 5 years and can be further extended after this time.
NVIDIA support includes:
- Access to NVIDIA GPU Cloud (NGC) Portal
- NVIDIA Cloud Management
- DGX Software Upgrades
- DGX Software Updates
- DGX Firmware Updates
- Hardware Support
- Hardware SLA (replacement parts) 1 day
- Software support — DGX OS image and full AI software including ML frameworks
- Enterprise Support Portal
- 24×7 Phone Support
- Access to NVIDIA Knowledgebase
You can also use consultancy with specialists from M Computers in Czech, Slovak and English.
With a combination of tuned hardware, software and NVIDIA support, NVIDIA DGX systems deliver significantly higher performance and acceleration in learning phase.