4 ms·

Can they just start investing in software so folks from ml/deep learning field can just get out from nvidia monopoly -.-

hajile 6y ago
Isn't that what Google's TPU equipment is for?
- csdtx 6y ago
  And get sued by Google? You aren't allowed to use them to train anything that could compete with Google's business or products as part of the usage agreement.
  - Teknoman117 6y ago
    So is the only reason to buy TPUs to learn about them and go work for Google or something? Why sell them if you can't use them for anything (as Google considers anything computing to be under their domain).
    - brandmeyer 6y ago
      > Why sell them They don't. GOOG rents them.
      - Teknoman117 6y ago
        Sorry, when I said sell I was thinking of the Coral Edge TPU board.
  - brandmeyer 6y ago
    Citation? I did a little digging of my own, because I found this claim to be uncharacteristic of any company I've ever worked for. I did find some restrictive clauses, but I don't think they are as broad as you are suggesting. https://cloud.google.com/terms/service-terms https://cloud.google.com/terms/service-terms Service Specific Terms -> AI and Machine Learning. You can't use Google's fully-trained models as a reference in order to train your own models. Lower-level uses of TPUs to train your own models from your own training data are unrelated to those restrictions. Certainly nothing in the Acceptable Use Policy looks anti-competitive to me. Obvious disclaimer is obvious: IANAL.
dragontamer 6y ago
NVidia's advantage is the tensor units. Not just software but also hardware. MI100 finally brings hardware accelerated matrix-multiplication to the table (bfloat16, FP16, and even FP32 matrix multiplication). Now that MI100 has matrix-multiplication units, now comes the part where that is distributed to the software. (Hooking up TensorFlow to use them). -------- Without Matrix Multiplication units, there was really no point even writing software (aside from maybe preparation of the eventual rise of AMD's matrix-multiplication routines). But 6800 XT won't have matrix-multiplication units. Only the CDNA architecture has those. So I'm not entirely sure if 6800 XT would even be an adequate competitor to deep learning applications, even if ROCm supported it entirely.
- hedgehog 6y ago
  It's got nothing to do with hardware, so far NVIDIA has been the only vendor to take deep learning seriously. AMD GPUs have at times been the best on paper for DL but always lacked real software support. A low cost 16GB card could be very useful but their strategy doesn't appear to have changed so today NVIDIA is still the only option.

hajile 6y ago

Isn't that what Google's TPU equipment is for?

csdtx 6y ago
And get sued by Google? You aren't allowed to use them to train anything that could compete with Google's business or products as part of the usage agreement.
- Teknoman117 6y ago
  So is the only reason to buy TPUs to learn about them and go work for Google or something? Why sell them if you can't use them for anything (as Google considers anything computing to be under their domain).
  - brandmeyer 6y ago
    > Why sell them They don't. GOOG rents them.
    - Teknoman117 6y ago
      Sorry, when I said sell I was thinking of the Coral Edge TPU board.
- brandmeyer 6y ago
  Citation? I did a little digging of my own, because I found this claim to be uncharacteristic of any company I've ever worked for. I did find some restrictive clauses, but I don't think they are as broad as you are suggesting. https://cloud.google.com/terms/service-terms https://cloud.google.com/terms/service-terms Service Specific Terms -> AI and Machine Learning. You can't use Google's fully-trained models as a reference in order to train your own models. Lower-level uses of TPUs to train your own models from your own training data are unrelated to those restrictions. Certainly nothing in the Acceptable Use Policy looks anti-competitive to me. Obvious disclaimer is obvious: IANAL.

dragontamer 6y ago

NVidia's advantage is the tensor units. Not just software but also hardware. MI100 finally brings hardware accelerated matrix-multiplication to the table (bfloat16, FP16, and even FP32 matrix multiplication). Now that MI100 has matrix-multiplication units, now comes the part where that is distributed to the software. (Hooking up TensorFlow to use them). -------- Without Matrix Multiplication units, there was really no point even writing software (aside from maybe preparation of the eventual rise of AMD's matrix-multiplication routines). But 6800 XT won't have matrix-multiplication units. Only the CDNA architecture has those. So I'm not entirely sure if 6800 XT would even be an adequate competitor to deep learning applications, even if ROCm supported it entirely.

hedgehog 6y ago
It's got nothing to do with hardware, so far NVIDIA has been the only vendor to take deep learning seriously. AMD GPUs have at times been the best on paper for DL but always lacked real software support. A low cost 16GB card could be very useful but their strategy doesn't appear to have changed so today NVIDIA is still the only option.