5 ms·
Seems to be good speedup overall relative to 2080 Ti (including FP16: see relatives 2080 Ti v Titan: https://lambdalabs.com/blog/2080-ti-deep-learning-benchmark
by liuliu 6y ago
Seems to be good speedup overall relative to 2080 Ti (including FP16: see relatives 2080 Ti v Titan: https://lambdalabs.com/blog/2080-ti-deep-learning-benchmarks/ https://lambdalabs.com/blog/2080-ti-deep-learning-benchmarks...). This suggests we should see another Titan card that is even more expensive in the pipeline given the FP16 performance? Or maybe TF32 performance is going to be what NVIDIA promotes in this generation (only if they have better number than FP16?)?
- jjoonathan 6y agoHere's hoping for an A100 titan with un-nerfed FP64. The 3090 is twice as nerfed as previous generations, which were also bad at 1:32. Now it's 1:64 :(
- p1esk 6y agoFP64 is not needed for deep learning.
- jjoonathan 6y agoWhat makes you think I'm doing deep learning?
- p1esk 6y agoYou’re commenting on the post called “Benchmarking deep learning workloads”.
- deleted 6y ago[deleted]
- Dylan16807 6y agoThe FP64 units are a separate addition that eat a lot of die space, right? I wouldn't use the word "nerf" for the tradeoff between having more SMs versus having more features in the SMs.
- jjoonathan 6y agoThey eat die space but not TDP.
- lsb 6y agoWhat workloads do you run that require fp64?
- jjoonathan 6y agoSimulation.
- bitL 6y agoRadeon VII is the best bang for the bucks in the FP64 space, then older K80s. Titan V/V100 are still expensive.
- jjoonathan 6y agoYep! I've got CUDA, so I still have to pay the green tax.
- nullifidian 6y agoIt seems that Radeon VII and Titan V are the last cards with decent fp64 performance for foreseeable future. Both Nvidia and AMD now basically have different architectures for their consumer and data-center products.
- jjoonathan 6y agoYep, sure looks that way. I'll still be dreaming of a Titan A!