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.