NVIDIA DGX A100

The NVIDIA DGX A100 is the 3rd generation of the world’s most advanced, purpose-built artificial intelligence and data analytics system. It represents a revolution in enterprise data centres by providing an infrastructure that unifies AI and data analytics applications.

55 296

GPU cores

up to 640 GB

GPU memory

128

CPU cores

up to 2 TB

RAM

1,92 TB

NVMe for OS

30 TB

NVMe for data

Hardware

Parametr	NVIDIA DGX A100 320 GB	NVIDIA DGX A100 640 GB
GPUs	8× NVIDIA A100 SXM4 40 GB	8× NVIDIA A100 SXM4 80 GB
GPU memory	320 GB total	640 GB total
CPU	2× AMD Epyc 7742 (64 cores, 2.25GHz)
Výkon (tensor operace)	5 PetaFLOPS
# CUDA jader	55 296
# Tensor jader	3 456
Multi-instantce GPU	56 instancí
RAM	1 TB	2 TB
HDD	OS: 2× 1.92 TB NVMe data: 15 TB (4× 3.84 TB) NVMe	OS: 2× 1.92 TB NVMe data: 30 TB (8× 3.84 TB) NVMe
Network	8x 200 Gb/s HDR InfiniBand 2x 200 Gb/s Ethernet	8x 200 Gb/s HDR InfiniBand 4x 200 Gb/s Ethernet
Max. spotřeba	6.5 kW
Provedení	rack, 6U
Technická specifikace	Stáhnout datasheet

Software

But what is much more interesting is the aforementioned software features of the offered NVIDIA machines. They all offer pre-installed and especially performance-tuned machine learning environments (e.g. Caffe, or Cafe 2, Theano, TensorFlow, Torch, 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)

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/

Support

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 extended beyond this period.

NVIDIA support covers:

Dispatch a customer engineer onsite for field replacement unit (FRU) replacement
Run onboard diagnostic tools (from remote) to troubleshoot system issues
Remote software support
Access to the latest software updates and upgrades
Direct communication with NVIDIA support experts
A private NGC container repository for accessing NVIDIA-optimized AI software with powerful sharing and collaboration features for your organization
A searchable knowledge base with how-to articles, application notes, and product documentation
Rapid response and timely issue resolution through a support portal and 24×7 phone access
Lifecycle support for NVIDIA DGX systems’ AI software
Replacement of defective HW components by the next working day
DGX Systems Appliance Support Services Terms and Conditions
End-user license agreement (EULA)

Through a combination of tuned hardware, software and NVIDIA support, NVIDIA DGX delivers significantly higher performance and acceleration in the learning phase of machine learning applications:

Applications performance comparison

GPU vs. CPU: Data analysis

A100 vs. V100: Model training

Through a combination of tuned hardware, software and NVIDIA support, NVIDIA DGX delivers significantly higher performance and acceleration in the learning phase of machine learning applications:

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.

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NVIDIA Deep Learning Institute

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.

More information

M Computers represents NVIDIA on the Czech market in the Enterprise area of computing accelerators and AI systems.

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.

Testing

To test the performance and especially the speed of deployment of ML and AI applications, we have the NVIDIA DGX Station available via NVIDIA Tesla Test Drive program as well as 2× NVIDIA Tesla V100 and NVIDIA Tesla T4. If you are interested in our testing offer, please fill out this form.

Kamila Jeřábková

NVIDIA, Lenovo servers

M: 734 161 516
kamila.jerabkova@mcomputers.cz

NVIDIA DGX A100

The NVIDIA DGX A100 is the 3rd generation of the world’s most advanced, purpose-built artificial intelligence and data analytics system. It represents a revolution in enterprise data centres by providing an infrastructure that unifies AI and data analytics applications.

Hardware

Software

NVIDIA GPU Cloud (NGC)

Support

Applications performance comparison

GPU vs. CPU: Data analysis

A100 vs. V100: Model training

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.

NVIDIA Deep Learning Institute

M Computers represents NVIDIA on the Czech market in the Enterprise area of computing accelerators and AI systems.

Testing

Kamila Jeřábková

References

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