11–20 of 104 posts
Edit: clarified that I am referring to slower relative performance
Can someone explain the difference between fp16 and fp32 in these benchmarks because the difference is pretty dramatic. I assume it's floating point precision(?) but why would lower precision be slower relatively on the 3090? For training jobs how does the precision impact accuracy of the model? Edit: clarified that I am referring to slower relative performance
Can someone explain the difference between fp16 and fp32 in these benchmarks because the difference is pretty dramatic. I assume it's floating point precision(?) but why would lower precision be slower relatively on the 3090? For training jobs how does the precision impact accuracy of the model? Edit: clarified that I am referring to slower relative performance
Seems like a good speedup relative to the Titan, especially for the money. I’d be interested to see the performance relative to the 3080 though. There are obviously vram limitations with the 3080 but it would still be interesting to see the difference in raw compute performance. In games the 3090 only gives a 15% performance bump relative to the 3080. If that pattern holds for machine learning tasks there is probably…
Could you kindly advise what kind of computer would make sense to purchase to begin learning about ML? I was assuming I'd get a 3080. Should I get a case that could potentially house 2 x 3080's? Does the case require any special cooling considerations, or just whatever will fit the cards? What CPU would you get?
Can someone explain the difference between fp16 and fp32 in these benchmarks because the difference is pretty dramatic. I assume it's floating point precision(?) but why would lower precision be slower relatively on the 3090? For training jobs how does the precision impact accuracy of the model? Edit: clarified that I am referring to slower relative performance
I just want to know how they installed the new nvidia cuda drivers without borking their Ubuntu/tf install.
+ tf-nightly and other python libraries installed through pipenv
Seems like a good speedup relative to the Titan, especially for the money. I’d be interested to see the performance relative to the 3080 though. There are obviously vram limitations with the 3080 but it would still be interesting to see the difference in raw compute performance. In games the 3090 only gives a 15% performance bump relative to the 3080. If that pattern holds for machine learning tasks there is probably…
If this isn't OT... Could you kindly advise what kind of computer would make sense to purchase to begin learning about ML? I was assuming I'd get a 3080. Should I get a case that could potentially house 2 x 3080's? Does the case require any special cooling considerations, or just whatever will fit the cards? What CPU would you get?
GPUs are only really required in ML if you want to do deep neural network stuff. You can do plenty in CPU on reasonable data sets using any modern laptop.
Can someone explain the difference between fp16 and fp32 in these benchmarks because the difference is pretty dramatic. I assume it's floating point precision(?) but why would lower precision be slower relatively on the 3090? For training jobs how does the precision impact accuracy of the model? Edit: clarified that I am referring to slower relative performance
Can someone explain the difference between fp16 and fp32 in these benchmarks because the difference is pretty dramatic. I assume it's floating point precision(?) but why would lower precision be slower relatively on the 3090? For training jobs how does the precision impact accuracy of the model? Edit: clarified that I am referring to slower relative performance
FP16 is faster (units are images per second)