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--- cluster:181 [2019/08/13 08:10]
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+++ cluster:181 [2019/08/13 08:13]
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 |  FP64?  |  -  |  some  |  -  |  some  |  -  |  yes  |  yes  |  -  |double precision|
-A lot of information comes from this web site [[https://blog.exxactcorp.com/whats-the-best-gpu-for-deep-learning-rtx-2080-ti-vs-titan-rtx-vs-rtx-8000-vs-rtx-6000/|Best GPU for deep learning]].  Deep learning (training and inference) are driving the GPU models more towards single precision (FP32) or even half precision (FP16) to speed up training. Double precision models (the P100 and V100) are scarce but there is also a scientific drive toward mixed precision applications (FP64/FP32 or FP32/FP16).
+A lot of information comes from this web site [[https://blog.exxactcorp.com/whats-the-best-gpu-for-deep-learning-rtx-2080-ti-vs-titan-rtx-vs-rtx-8000-vs-rtx-6000/|Best GPU for deep learning]].  Deep learning (training and inference) are driving the GPU models more towards single precision (FP32) or even half precision (FP16) to speed up training. Double precision models (the P100 and V100) are still available but there is a scientific drive towards mixed precision applications (FP64/FP32 or FP32/FP16 or even integer mixes).
 Bench statistics (Nvidia GTX 1070 is about 100% baseline) from this web site [[https://gpu.userbenchmark.com/Faq/What-is-the-effective-GPU-speed-index/82|External Link]]

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