Currently, GPU servers are mainly used in different scenarios such as scientific computing and video encoding and decoding. It can provide applications with extraordinary accelerated computing capabilities and can offload application computing-intensive workloads to the GPU.
From the user's perspective, the running speed of the application has been significantly improved. Its fast and stable computing capabilities are deeply recognized by users.
When users choose a GPU server, they first need to consider the business needs and match the appropriate GPU model. For example, in HPC high-performance computing, the choice needs to be based on accuracy. If the P40 or P4 model is used, it is not very suitable.
Selecting a GPU server mainly considers the following three factors:
First of all, when selecting a GPU server, you also need to consider the maturity of the GPU cluster system and engineering efficiency. For example, DGX, a GPU-integrated supercomputer, has a very mature operating system driver, and the efficiency of this type of server is much higher.
Secondly, enterprises need to choose the server corresponding to T4 or P4 based on the actual situation on the edge server, and need to consider the server application scenario.
When enterprises do Inference at the center, they need to configure V100 servers and consider factors such as server throughput, usage scenarios, and quantity.
Again, when choosing a GPU server, you also need to consider your customer base and your own technical operation and maintenance capabilities. For some large enterprises, which have their own operation and maintenance teams, they will choose a relatively unified PCI-e server; for small and medium-sized enterprises with weak technical capabilities, their standards for selecting GPU servers have also dropped.
The selection factors of GPU server are very critical and very technical. When making a choice, customers need to make comprehensive choices based on their own circumstances, user groups and even different business scenarios
