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Today, I will talk to you about how to use gpu CVM. Many people may not know much about it. In order to make you understand better, the editor has summarized the following for you. I hope you can get something from this article.

GPU CVM provides flexible GPU services on the public cloud, which can help users quickly use GPU acceleration services and greatly simplify the complexity of deployment and operation and maintenance. GPU CVM is mostly suitable for AI deep learning, scientific computing, video processing, graphics visualization and other application scenarios.

How to use gpu CVM

Learning AI reasoning

Powerful AI reasoning and fixed-point computing capabilities meet the computing power requirements in deep learning (especially reasoning) scenarios. The perfect combination with the elastic computing ecology provides a general solution for both online and offline scenarios. Use with CCS to simplify the complexity of deployment and operation and maintenance, provide resource scheduling services, and simplify user deployment.

Online rendering in the cloud

Online real-time rendering, through the cloud desktop to quickly intervene in GA1, to get a better visual and control experience. Online real-time rendering and graphics and image editing can also be achieved through the remote desktop connection protocol, and multiple devices can be used to access it anywhere. Access and use the intranet at any time to protect data security.

GPU Computing acceleration

Provide acceleration for deep learning and meet the computing power requirements of large-scale parallel floating-point computing in general GPU computing scenarios such as deep learning with single-precision floating-point computing power. Combined with elastic computing ecology and used with container services, it simplifies the complexity of deployment and operation and maintenance, and provides resource scheduling services.

Floating-point operation acceleration

It meets the computing power requirements of large-scale parallel floating-point operations in general GPU computing scenarios such as deep learning. Meet the needs of high-performance computing scenarios such as scientific computing. Direct communication between GPU can be realized through PCI bus, which greatly reduces the communication delay between GPU.

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