Which setup is better for ML

3080 x 2
a single 3090?
Can tensor flow aggregate vram across 2 cpus?

I’m assuming you mean gpu’s. And even if it could, you would probably need NVLink for that. But NVidia axed it on 3080… so NVidia says no… :wink:
DMA over PCIe still may be possible with BAR promises… but real question is where you gonna get 2x3080? I mean, one is miracle as I understand :wink:

3090 of course, as if you only want to train one model at a time, if you want to train 2 models in parrallel, then 2 3080

Data crunching wise more VRAM is important if you’re dealing with large amounts of data, two 3080s won’t share memory however you could in theory like previous GTX/RTX 20-series get more value for compute dollars as long as you don’t reach the VRAM wall. It really depends upon the workflow, if you’re doing real-time crunching the faster GPU is going to make a huge difference.

In my opinion/experience crunching data sets such as financial(stock data) non-real-time memory becomes a limiting factor based on the larger data set–real-time crunching with a slight delay is workable with a mid-tier GPU. On the other hand if you’re doing data sets on fluid or air dynamics, you’ll be wanting that extra VRAM. (I’ve managed to get a reasonable amount of work done with a 1070/2070, the key point is to try to scale the type of workflow to the hardware)

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