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One significant advantage AMD might have is that they have chosen to provide full rate FP16 support: http://www.tomshardware.com/news/amd-radeon-rx-vega-64-spe

One significant advantage AMD might have is that they have chosen to provide full rate FP16 support: http://www.tomshardware.com/news/amd-radeon-rx-vega-64-specs-availability,35112.html http://www.tomshardware.com/news/amd-radeon-rx-vega-64-specs... ...unlike nVidia: http://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-1080-and-1070-founders-edition-review/5 http://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-1... While that's something that developers have to explicitly support, it will be interesting to see what happens when they do.

gcp 9y ago
At 21TFLOPS for 400 USD it has good potential in the machine learning field, yes. AFAIK AMD is busy pushing out support for MIOpen into Tensorflow etc.
- mamon 9y ago
  For machine learning Tensor Cores in the upcoming Volta GPUs seem much better idea, delivering 120 TFLOPS. Although the price would be a multiple of 400 USD, for sure.
  - jlebar 9y ago
    > For machine learning Tensor Cores in the upcoming Volta GPUs seem much better idea, delivering 120 TFLOPS. Yes, but the real-world benchmark numbers nvidia published show a much smaller speedup than one would expect if one looked only at the 120TFLOPS number. This is because (a) "tensor core" is just a fancy name for "fast 4x4 matmul instruction" -- it's not applicable to everything you might do on the GPU, and (b) memory bandwidth did not increase commensurate with compute speed. (I work on the XLA GPU compiler in TensorFlow.)