The department of the university where I am currently pursuing a Ph.D. has a machine with the following specifications:
|OS||Windows 10 Enterprise LTSC|
|Processor||Intel® Xeon Gold 5120 CPU @ 2.20GHz 2.19GHz (x2 processors)|
|RAM Memory||96 GB (94.7 GB for real use)|
|Memory||2.5 TB (2TB HDD + 500GB SSD)|
|GPU 1||Nvidia Quadro P5000 with 16 GB|
|GPU 2||Nvidia Quadro RTX 8000 with 48 GB|
The problem is that the deep learning tasks to be performed by both the principal researchers and other Ph.D. students has been increasing in recent months, thus considering the possibility of increasing the resources of the current machine. In this sense, in an attempt to find out what would be the best option, I have reviewed several resources such as some of the posts published by Tim Dettmers (Post 1, Post 2). However, I have a few questions such as the following:
- Given our situation, would it be better to continue using the shared workstation approach that we are currently using or move to a cluster of GPUs?
- Our actual single Nvidia Quadro RTX 8000 offers us 48 GB of memory, being very valuable for training and loading heavy models. In this sense, I am afraid of moving to the state-of-the-art RTX 3080 or 3090 cards, which individually contain much less memory (10GB and 24 GB, respectively). In case of building a cluster with these latter type of cards, is the final available memory the result of adding the memories of each of the cards? Or is not as simple as that? Sorry if this is a very basic question.
- Would you consider as an option to include a second Nvidia Quadro RTX 8000 replacing the P5000? Or would it be better to sell both GPUs and get, for example, 2 RTX 3090? From what I have been able to read in the posts I have quoted above, it seems that the second option would be the most suitable one...
Any recommendations are welcome. Thank you very much in advance for your help.