The NVIDIA DGX B200 is nvidia’s sixth generation rackmount supercomputer for training artificial intelligence and machine learning models. It provides a versatile platform built on the world’s most powerful graphics chip.
B200 180GB
TSMC N4P transistors
GPU memory
RAM
NVMe for OS
NVMe for data
| Parametr | NVIDIA DGX B200 |
|---|---|
| GPU | 8x NVIDIA B200 |
| GPU Memory | 8x 192GB HBM3e (1536 GB celkem)* |
| CPU | až 2x Intel Xeon Platinum 8570 2.1 GHz (56 jader)* |
| Výkon FP8/INT8 Tensor | 72 petaFLOPS |
| # CUDA jader | TBA |
| # Tensor jader | TBA |
| RAM | až 4TB DDR5 |
| Úložiště | OS: 2x 1.9TB NVMe M.2 Storage: 8x 3.84TB NVMe U.2 |
| Síť | 4x OSFP pro 8x single-port NVIDIA ConnectX-7 VPI (400Gb/s InfiniBand/Ethernet) 2x dual-port QSFP112 NVIDIA BlueField-3 DPU (400Gb/s InfiniBand/Ethernet) |
| Max. spotřeba | ~ 14.3 kW |
| Formát | 10U rack |
* the exact details are not yet known, specific configuration and offer will be added.
Comparison of previous and current generation DGX stations in several tasks to show the significant performance gains in some applications.
But what is much more interesting is the aforementioned software features of the offered NVIDIA machines. All of them offer pre-installed and especially performance-tuned machine learning environments (e.g. Caffe or Cafe 2, Theano, TensorFlow, PyTorch, or MXNet) or intuitive data analytics environments (NVIDIA Digits). All neatly packaged in Docker containers, freely downloadable on the NVIDIA GPU Cloud (NGC). 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.
NVIDIA GPU Cloud (NGC) represents the 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 the NGC repository, moving it on the DGX system, and downloading and running the Docker container. The individual development environments – versions of all included libraries and frameworks, settings of environment parameters – are updated and optimized by NVIDIA 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 as intended. The customer has hundreds of developers ready to help. Support is part of NVIDIA DGX purchase. It is available for 3 – 5 years and can be further extended at the end of that period.
NVIDIA offers special programs for DGX systems and Tesla accelerators for EDU organizations or start-up companies. Thanks to the international collaboration between NVIDIA and IBM Global Financing, preferential financing in the form of operating leases is available for DGX models.
Last year, it was the first company in Central and Eastern Europe to receive the highest ELITE PARTNER status, as well as two NVIDIA AI Innovator and NVIDIA AI Champion awards.
The NVIDIA Deep Learning Institute (DLI) offers both online and hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing.
We have a wide range of NVIDIA GPU cards and other NVIDIA systems available to test performance and process demanding tasks.
If you are interested in our testing offer, please fill out this form.
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