There is no formal industry benchmark that defines consumer and data center GPUs. However, there are hardware and processing speed differences between GPU offerings that separate data center and PC-based use cases.
A traditional consumer GPU, such as the Nvidia GeForce GTX Titan Xp, clocks in at up to 0.38 teraflops (Tflops) in 64-bit floating point processing, while the enterprise-class Nvidia Tesla V100 runs as high as 7.8 Tflops. Each GPU has different architectures, number of Compute Unified Device Architecture (CUDA) cores, memory bandwidth and overall functionality.
Vendors classify GPUs by use case and processing speed, so you should identify which workload you need the GPU for and then research the best options. Consumer GPUs are suitable for data centers with low bandwidth and power requirements, testing software, or installation in off-the-shelf PCs.
You should investigate high-end GPUs, however, if you deal with image classification, big data processing, the internet of things or machine learning. You can maximize your hardware investment if your server includes a GPU expansion chassis. Data center GPU cards can provide ample graphics processing capabilities for the most demanding workloads.
Nvidia's data center GPU offerings include the Quadro and Tesla product lines. With Nvidia's recent end-user licensing agreement updates, which ban the use of consumer GPUs in the data center, your selection might be limited to high-end GPUs -- especially if the GeForce and Titan drivers and software aren't licensed for data center use.
Implement preinstalled GPUs in the data center
An increasing number of servers are available with preinstalled and preconfigured GPUs. This enables GPU introduction to be a regular part of the server technology refresh cycle.
There are many server models with various data center GPU options available for preinstallation. For example, the Tesla V100 is available on the Dell EMC PowerEdge C4140 and PowerEdge R740, while the Tesla P40 is preinstalled on the Lenovo NeXtScale nx360, plus the ThinkSystem SD530 and Lenovo D2.
For non-Nvidia offerings, the AMD FirePro S7100X is available on the Dell PowerEdge M630 and the HPE ProLiant WS460c Gen9.
Your selection of preinstalled GPUs will depend on what server hardware is already in your data center and your current processing needs. Someone on your team should be familiar with graphics libraries and languages, such as OpenGL or CUDA, so they can program the GPUs and update any associated software.
Dig Deeper on Data center server infrastructure and OSes
Related Q&A from Stephen J. Bigelow
While the Windows Admin Center is one way to manage the Azure Stack HCI platform, you can also use traditional, battle-tested tools. Continue Reading
There are many tools available on the AWS Marketplace for QA testing, making it difficult to determine where to begin. What should an enterprise look... Continue Reading
Hyper-converged infrastructure that runs on Windows Server is not a new concept, but Microsoft's Azure Stack HCI program has one big difference from ... Continue Reading
Have a question for an expert?
Please add a title for your question
Get answers from a TechTarget expert on whatever's puzzling you.