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Introduction​

A cost-effective way to run high-performance computations

Level up your cloud servers with GPU accelerators. Graphical processing units are mostly used for deep machine learning, architectural visualization, video processing, and scientific computing.

GPU Image 1
GPU Image 2

TESLA​

Tesla GPU

We provide servers that are specifically designed for machine learning and deep learning purposes, and are equipped with the following distinctive features:

  • Modern hardware based on the NVIDIA GPU chipset, which has a high operation speed.
  • The newest Tesla V100 & T4 cards with their high processing power.
  • Bundled with CUDA technology

CUDA​

Parallel computing architecture designed by NVIDIA. The implementation of this architecture allowed a significant increase in the performance of GPU computing.

Advantages of CUDA​

  • Using the common programming language C
  • Applying technologies such as Parallel Data Cache and Thread Execution Manager
  • Default libraries for FFT and BLAS
  • Unification of hardware and software parts for GPU computing
  • Multi-GPU Support
  • Hardware Debugging
  • Excellent scalability to different projects
  • Special CUDA driver

All our GPU plans support NVIDIA® CUDA-capable and cuDNN with the latest version.

  • Pre-installed with Nvidia-Docker2
  • Includes NVIDIA® GPU Cloud (NGC) Catalog CLI, a command-line interface for managing content within the NGC Registry
  • Includes development tools based on the programming languages Python 2, Python 3, and C++

You can choose OS flavors like Ubuntu 16, Ubuntu 18.04, CentOS 7, or Windows 2016 with NVIDIA TESLA V100 and T4 GPU Servers.