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Corporate services Technical characteristics
NVIDIA hardware and software systems
- NVIDIA BigData DGX Supercomputers with NVIDIA Tesla V100 32G Graphics Cards
- Use of NVIDIA KVM virtualization to create cloud servers with required number of video cards.
- Use of NVIDIA's Ubuntu-based DGX OS operating system on hosts and virtual machines
- NVIDIA Docker containerization system is pre-installed on each virtual machine
- Full support of optimized GPU–accelerated NVIDIA GPU Cloud application container catalog
Amazing performance
- Productivity up to 1000 TERAFLOPS (for deep learning tasks)
- 40.960 CUDA cores to speed up video card performance
- 5120 Tensor Cores to accelerate matrix operations that lie at core of Artificial Intelligence
- Up to 10 times faster than connecting video cards through PCIe interface because of service bus NVidia NVLink with capacity of 300 Gbit/s
- Up to 4 times faster training of deep learning algorithms compared to other GPU-based systems
Creating a platform for deep learning goes far beyond than just a choice of a provider and graphics processors. The desire to introduce artificial intelligence in work of enterprise implies careful selection and integration of a complex set of software and hardware hardware. GPU SuperComputer accelerates this process, providing a ready-made solution that allows to process data and get results within the shortest possible time.
Technical characteristics
NVIDIA hardware and software systems
- NVIDIA BigData DGX Supercomputers with NVIDIA Tesla V100 32G Graphics Cards
- Use of NVIDIA KVM virtualization to create cloud servers with required number of video cards.
- Use of NVIDIA's Ubuntu-based DGX OS operating system on hosts and virtual machines
- NVIDIA Docker containerization system is pre-installed on each virtual machine
- Full support of optimized GPU–accelerated NVIDIA GPU Cloud application container catalog
Amazing performance
- Productivity up to 1000 TERAFLOPS (for deep learning tasks)
- 40.960 CUDA cores to speed up video card performance
- 5120 Tensor Cores to accelerate matrix operations that lie at core of Artificial Intelligence
- Up to 10 times faster than connecting video cards through PCIe interface because of service bus NVidia NVLink with capacity of 300 Gbit/s
- Up to 4 times faster training of deep learning algorithms compared to other GPU-based systems
Configurations and pricing
1 x Tesla V100 32GB
- 10 cores
- 61GB RAM
- 250GB SSD
1 999
rubles / day
49 000
rubles / month
2 x Tesla V100 32GB
- 20 cores
- 122GB RAM
- 500GB SSD
3 999
rubles / day
99 000
rubles / month
4 x Tesla V100 32GB
- 40 cores
- 215GB RAM
- 1000GB SSD
5 999
rubles / day
199 000
rubles / month
8 x Tesla V100 32GB
- 80 cores
- 490GB RAM
- 2000GB SSD
9 999
rubles / day
259 000
rubles / month
There are many GPU-accelerated solutions on the market today. But only NVIDIA BigData DGX-based SuperComputer GPU unlocks the full potential of the most advanced NVIDIA Tesla V100 accelerators and uses the new NVIDIA NVLink technology and the Tensor core architecture. The service provides 4-fold acceleration of deep learning algorithms training compared to other GPU-based systems. This is possible through the use of NVIDIA GPU Cloud catalog that provides access to optimized versions of currently most popular frameworks.
Main supported technologies
High performance computingDeep learning and machine learningVisualizationInferenceSmart citiesInfrastructureMedicine
HOOMD-blue
RELION
PIConGPU
QMCPACK
Parabricks
NAMD
MILC
Microvolution
LAMMPS
GROMACS
GAMESS
CHROMA
BigDFT
PGI Compilers
Lattice Microbes
CANDLE
MATLAB
VMD
RAPIDS
TensorRT Inference Server
PyTorch
TensorRT
TensorFlow
DIGITS
MXNet
NVCaffe
Kaldi
Deep Cognition Studio
Dotscience Runner
MapR PACC
Cuda
Cuda GL
MATLAB
Torch
Theano
Microsoft Cognitive Toolkit
Caffe2
Kinetica
H2O Driverless AI
Chainer
PaddlePaddle
![OmniSci [MapD]](/upload/iblock/807/Snimok_ekrana_2019_06_11_v_14.41.29.jpg "OmniSci [MapD]")
OmniSci [MapD]
VMD
ParaView IndeX
Clara Render Server
ParaView
ParaView OptiX
ParaView Holodeck
IndeX
Smart Parking Detection
DeepStream
CT Organ Segmentation AI
Cuda
Cuda GL
TensorRT
TensorRT Inference Server
Kaldi
Transfer Learning Toolkit
Smart Parking Detection
DeepStream
Dotscience Runner
MapR PACC
Cuda
Cuda GL
DCGM Exporter
Device Plugin
CT Organ Segmentation AI
Clara Render Server
HOOMD-blue
RELION
PIConGPU
QMCPACK
Parabricks
NAMD
MILC
Microvolution
LAMMPS
GROMACS
GAMESS
CHROMA
BigDFT
PGI Compilers
Lattice Microbes
CANDLE
MATLAB
VMD
RAPIDS
TensorRT Inference Server
PyTorch
TensorRT
TensorFlow
DIGITS
MXNet
NVCaffe
Kaldi
Deep Cognition Studio
Dotscience Runner
MapR PACC
Cuda
Cuda GL
MATLAB
Torch
Theano
Microsoft Cognitive Toolkit
Caffe2
Kinetica
H2O Driverless AI
Chainer
PaddlePaddle
OmniSci [MapD]
VMD
ParaView IndeX
Clara Render Server
ParaView
ParaView OptiX
ParaView Holodeck
IndeX
Smart Parking Detection
DeepStream
CT Organ Segmentation AI
Cuda
Cuda GL
TensorRT
TensorRT Inference Server
Kaldi
Transfer Learning Toolkit
Smart Parking Detection
DeepStream
Dotscience Runner
MapR PACC
Cuda
Cuda GL
DCGM Exporter
Device Plugin
CT Organ Segmentation AI
Clara Render Server
Use NVIDIA and MTS experience in the field of deep learning for your project and there will be no need to spend extra time and money to get the needed results. Spend less time on setup and optimization and do more research.
Make innovations happen faster
High-performance training speeds up your productivity meaning that you can spend less time on launch of your solution in production use.
NVIDIA DGX-1 Delivers 140X Faster Deep Learning Training
Technical characteristics
NVIDIA hardware and software systems
- NVIDIA BigData DGX Supercomputers with NVIDIA Tesla V100 32G Graphics Cards
- Use of NVIDIA KVM virtualization to create cloud servers with required number of video cards.
- Use of NVIDIA's Ubuntu-based DGX OS operating system on hosts and virtual machines
- NVIDIA Docker containerization system is pre-installed on each virtual machine
- Full support of optimized GPU–accelerated NVIDIA GPU Cloud application container catalog
Amazing performance
- Productivity up to 1000 TERAFLOPS (for deep learning tasks)
- 40.960 CUDA cores to speed up video card performance
- 5120 Tensor Cores to accelerate matrix operations that lie at core of Artificial Intelligence
- Up to 10 times faster than connecting video cards through PCIe interface because of service bus NVidia NVLink with capacity of 300 Gbit/s
- Up to 4 times faster training of deep learning algorithms compared to other GPU-based systems
Customer feedback
Nowadays artificial intelligence technologies open up space for a variety of areas, ranging from finance, telecom, manufacturing, and ending with healthcare and education. Thanks to #СloudMTS, Russian companies and researchers will be able to use the NVIDIA DGX platform which is a powerful tool for developing neural networks.
Sergey Shatov
So far we are enjoying our experience. Instant access to latest version of Docker. We already played around with machine learning, pattern recognition and other typical tasks. The hardware works, there were no problems with software, the network did not fail. The pricing is reasonable. So far so good.
Ivan Muratov
Available services
FAQ
?Which CUDA versions are supported?
