Your data science team depends on computing performance to gain insights and innovate faster through the power of AI and deep learning.
Until now, AI supercomputing was confined to the data center, limiting the experimentation needed to develop and test deep neural networks prior to training at scale. Designed for your data science team, NVIDIA® DGX Station™ is the world’s fastest workstation for leading-edge AI development. This fully-integrated and optimized system enables your team to get started faster and effortlessly experiment with the power of a data center in your office.
Your real work is innovation and discovery. DGX Station is the only workstation with four NVIDIA® Tesla® V100 Tensor Core GPUs, integrated with a fullyconnected four-way NVIDIA NVLink™ architecture. With 500 TFLOPS of supercomputing performance, your entire data science team can experience over 2X the training performance of today’s fastest workstations.
Experiment faster and iterate more frequently for effortless productivity.
This groundbreaking solution offers:
Spend less time and money on configuration, and more time on data science. DGX Station can save you hundreds of thousands of dollars in engineering hours and lost productivity waiting for stable versions of open source code. Powered by the NVIDIA DGX Software Stack, DGX Station lets you to start innovating within one hour.
This integrated hardware and software solution allows your data science team to easily access a comprehensive catalog of NVIDIA optimized GPU-accelerated containers that offer the fastest possible performance for AI and data science workloads. It also includes access to NVIDIA DIGITS™ deep learning frameworks, HPC containers, third-party accelerated solutions, the NVIDIA Deep Learning SDK (e.g. cuDNN, cuBLAS, NCCL), NVIDIA CUDA® toolkit, RAPIDS open source libraries, and NVIDIA drivers. Built on container technology and powered by NVIDIA Container Runtime for Docker, this unified deep learning software stack simplifies your workflow, saving you days in re-compilation time when you need to scale your work and deploy your models in the data center or cloud. The same workload running on DGX Station can be effortlessly migrated to an NVIDIA DGX-1™, NVIDIA DGX-2™, or the cloud, without modification.
With DGX Station, you benefit from NVIDIA’s AI expertise, enterprisegrade support, extensive training, and field-proven capabilities that can jump-start your work for faster insights. Our dedicated team is ready to get you started with prescriptive guidance, design expertise, and access to our fully-optimized DGX Software Stack. You get an IT-proven solution backed by enterprise-grade support and a team of experts who can help ensure your mission-critical AI applications stay up and running.
With GPU-aware Kubernetes from NVIDIA, your data science team can benefit from industry-leading orchestration tools to better schedule AI resources and workloads. Data scientists can run compute workloads by scheduling and queuing jobs, running multiple jobs simultaneously, and easily monitoring GPU health. Eliminate any idle usage of GPUs, drive down the cost per training run, and maximize the productivity and return on investment for your data science team. Enjoy productive experimentation and spend more time focused on insight.
Need to deliver AI breakthroughs but lack the data center to develop them? With DGX Station, you don’t need to worry about the challenges and complexities of building an enterprise-scale infrastructure to access compute capacity.
DGX Station has the computing capacity of racks of servers in an office-friendly package and has the fastest deep learning system for your data science and AI research teams. Designed for the office and whisper-quiet, you can access water-cooled supercomputing performance at your fingertips. You can also instantly boost your productivity with a workstation that includes access to NVIDIA optimized AI and HPC software.
Innovate on your terms instead of waiting for compute cycles in the data center
| GPUs | 4X Tesla V100 |
| TFLOPS (Mixed precision | 500 |
| GPU Memory | 128 GB total system |
| NVIDIA Tensor Cores | 2,560 |
| NVIDIA CUDA® Cores | 20,480 |
| CPU | Intel Xeon E5-2698 v4 2.2 GHz (20-Core) |
| System Memory | 256 GB RDIMM DDR4 |
| Storage |
Data: 3X 1.92 TB SSD RAID 0 OS: 1X 1.92 TB SSD |
| Network | Dual 10GBASE-T (RJ45) |
| Display | 3X DisplayPort, 4K resolution |
| Additional Ports | 2x eSATA, 2x USB 3.1, 4x USB 3.0 |
| Acoustics | < 35 dB |
| System Weight | 88 lbs / 40 kg |
| System Dimensions | 518 D x 256 W x 639 H (mm) |
| Maximum Power Requirements | 1,500 W |
| Operating Temperature Range | 10–30 °C |
| Software |
Ubuntu Desktop Linux OS DGX Recommended GPU Driver CUDA Toolkit |