Why need a more gpu memory rtx than gtx?

I want to know reasons.


pc_1 : gtx 1080ti(11G), cuda-10, tensorflow-gpu==1.13
pc_2 : rtx 2080ti(11G), cuda-10, tensorflow-gpu==1.15
pc_3 : rtx 2080ti(11G), cuda-11.0, tensorflow-gpu==1.15
pc_4 : rtx 3080(10G), cuda-11.1, nvidia-tensorflow==r1.15.4-20.11

I’ve loaded a weight file using memory fraction 1.5GB on pc_1~pc_3. I tested that load same weight file using memory fraction 1.5GB on pc_4 yesterday, but couldn’t load. however, I could load it using memory fraction about 5.7GB on pc_4.

although same weight file, why require more gpu memory?
I couldn’t find solutions anywhere. I guess rtx 30xx serise or nvidia-tensorflow is the reason.

Hey @god.donghwan,

I am having similar gpu memory issue in my rtx 3080. I also tried using NGC-Tensorflow-Containers, but I am still facing the memory issue. The models are occupying more memory in 3080 as compared to 2080ti.

Did you find any reasons behind this?