public cloud compute  gpu

Cloud servers specially designed for processing massively parallel tasks

GPU instances integrate NVIDIA Tesla V100 graphic processors to meet the requirements of massively parallel processing. Since it is integrated into the OVHcloud solution, you get the advantages of on-demand resources and hourly billing. These cloud servers are adapted to the needs of machine learning and deep learning.

Powered by NVIDIA Tesla V100 GPUs

Designed for use in datacentres, these GPUs are among the most powerful on the market, and have proven invaluable in the fields of artificial intelligence (AI) and graphic computing.

NVIDIA GPU Cloud

To provide the best possible user experience, OVH and NVIDIA have partnered to offer a best-in-class GPU-accelerated platform, for deep learning and high-performance computing and ​artificial intelligence (AI). It is the simplest way to deploy and maintain GPU-accelerated containers, via a full catalogue. Find out more.

1-4 Tesla cards, with guaranteed performance

Tesla cards are delivered directly to the instance via PCI Passthrough, without a virtualisation layer, so their full power is dedicated to you. As a result, the hardware devotes all of its computing power to your application. Up to four cards can be connected, to further enhance their performance. 

NVIDIA Tesla V100 Features

Performance with NVIDIA GPU Boost

Bidirectional connection bandwidth

CoWoS Stacked HBM2 memory
  • double-precision, 7 teraFLOPS
  • simple-precision, 14 teraFLOPS
  • deep learning, 112 teraFLOPS
  • PCIe 32 GB/s
  • capacity, 16 GB HBM2
  • bandwidth, 900 GB/s

 

Use cases

Image recognition

Extracting data from images to classify them, identify elements, or build richer documents is now standard practice in many industries. Frameworks like Caffe2, combined with Tesla V100 GPUs, are a key part of this, for projects such as medical imaging, social networks, public protection and security.

Situation analysis

Real-time analysis is required in some cases, where an appropriate reaction is expected to face varied and unpredictable situations. This kind of technology is used for self-driving cars and internet network traffic analysis, for example. This is where deep learning comes in, to form neural networks that learn independently through a training stage.

Human interaction

In the past, people people learned to communicate with machines. In the modern era, machines are learning to communicate with people. Through speech recognition and the identification of emotions from sound and video, tools like Tensorflow are transforming these interactions, opening up a world of new opportunities.

Usage

1

Get started

Start your instance by choosing a T1 model and NGC image to suit you.

2

Configure

$ docker pull nvcr.io/nvidia/tensorflow
$ nvidia-docker run nvidia/tensorflow t1

3

Use

Your AI framework is ready for processing.

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Pricing Public Cloud

GPU billing

GPU instances are billed like all of our other instances, on a pay-as-you-go basis at the end of each month. The price depends on the size of the instance you have booted, and the duration of its use.