If we are running data from a larger project, will we choose a larger memory and CPU configuration? We may not pay much attention to the disk size and bandwidth of the server. After all, the exclusive servers that need larger CPU and memory configurations can be used for large data computing and graphics processing. In fact, we can use GPU virtual machines and GPU bare metal servers. In this article, we will introduce the differences between GPU servers.
1. Conceptual differences
GPU bare metal server It not only provides inexpensive and high-quality GPU physical cloud services, but also tailors them to large-scale offline computing scenarios such as AI training. Two sets of physical cloud clusters, 10G network and 25G network, are provided to support instant self-service delivery of the console. It also provides GPU bare metal servers. Bare Metal Servers are similar to dedicated physical servers on the cloud. On the basis of flexibility, they have high-performance computing capabilities comparable to traditional physical machines, and maintain secure physical isolation. The minute level delivery capability, disk scalability and downtime recovery capability provide rapid deployment and flexible expansion of elastic solutions for fast-growing enterprises.
GPU virtual machine It is a cloud host service based on mature cloud computing technology and exclusive access to high-performance GPU hardware. It greatly improves graphics processing and high-performance computing capabilities, and has the characteristics of flexibility, low cost, ease of use, etc. Effectively improve computing efficiency in graphics processing, scientific computing and other fields, and reduce IT cost investment. Currently, G1 (Tesla K80), G2 (Tesla P40), G3 (Tesla V100), G4 (Tesla T4) and G7 (8-card T4 and 8-card V100) models are available.
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2. Application field
GPU virtual machine and GPU bare metal server actually have relatively high configuration hardware and meet the requirements of large-scale cloud computing business. Improve graphics and video processing capabilities. From the perspective of price and concept, the bare metal server will be more advanced than the virtual machine, and more suitable for large-scale project operation.
The CPU, memory, and local disk configurations on the GPU ECS are combined proportionally with the number of GPUs to ensure that the GPU maintains high computing performance. You can improve or reduce the virtual machine configuration at any time, or expand or reduce the resource ownership according to business needs. GPU bare metal servers, system disks and data disks all use RDMA based scalable RSSD cloud disks, which can reach up to 480000 IOPS, better support AI large-scale training clusters, RSSD cloud disks can be accessed quickly, and better support the storage of large-scale training data.
The bare metal server is applicable to the training of artificial intelligence algorithms such as image recognition, unmanned driving, speech recognition and reasoning application calculation; In scientific computing, it is applicable to fluid dynamics, computational finance, molecular dynamics, environmental analysis, etc. In terms of video encoding and decoding, it is applicable to transcoding acceleration services, such as live broadcast, online education and other scenarios; In terms of rendering, scenes mainly include cloud games, video structuring, etc.
Therefore, for ordinary businesses, we will use ordinary cloud servers and virtual machines, GPU hosts and servers for building stations, which are suitable for the fields of artificial intelligence, large-scale computing finance and environmental analysis.
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