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It isn't the hobbyists who are making sure that PyTorch and other frameworks runs well on these chips, but teams of engineers who work for NVIDIA, AMD, Intel, e

It isn't the hobbyists who are making sure that PyTorch and other frameworks runs well on these chips, but teams of engineers who work for NVIDIA, AMD, Intel, etc. who are doing this as their primary assigned jobs, in exchange for money from their employer, who are paying those salaries because they want to sell chips into the enormous demand for running PyTorch faster. Hobbyist and open-source are definitely not synonyms.

Eisenstein 3y ago
People don't usually get employed to make things with no demand, and people who work for companies with a budget line don't really care how much the nVidia tax is. You can thank hobbyists for creating a lot of demand for compatability with other cards.
- kiratp 3y ago
  There are so many billions of dollar being spent on this hardware that everyone other than Nvidia is doing everything they can to make competition happen. Eg: https://www.intel.com/content/www/us/en/developer/videos/optimize-dl-workloads-intel-optimized-pytorch.html https://www.intel.com/content/www/us/en/developer/videos/opt... https://www.intel.com/content/www/us/en/developer/tools/oneapi/optimization-for-tensorflow.html https://www.intel.com/content/www/us/en/developer/tools/onea... https://developer.apple.com/metal/tensorflow-plugin/ https://developer.apple.com/metal/tensorflow-plugin/ Large scale opensource is, outside of a few exceptions, built by engineers paid to build it.
- roenxi 3y ago
  There is huge demand for AMD cards that can efficiently multiply matrices together. The issue is that while there are currently isolated cases where people can make them do that, it doesn't seem to be possible at the scale that it needs to happen at. AMD are being dragged along by the market. Willingly, they aren't fighting it, but their focus has been on other areas.
  - viewtransform 3y ago
    <but their focus has been on other areas.> They've shifted a large pool of experienced engineers from legacy software projects to AI and moved the team under a veteran Xilinx AI director. Fingers crossed we should see significant changes in 2024.
    - Flameancer 3y ago
      As a new owner of a 7800XT I’m excited.
  - deleted 3y ago
    [deleted]
  - iopq 3y ago
    Look at the earnings call: https://www.fool.com/earnings/call-transcripts/2023/08/01/advanced-micro-devices-amd-q2-2023-earnings-call-t/ https://www.fool.com/earnings/call-transcripts/2023/08/01/ad... it's literally ALL AI, server, enterprise talk - AI is mentioned 64 times AMD literally doesn't care about gaming anymore, server is their primary focus
- johngossman 3y ago
  I can only point you to cloud financial results and the huge cost of the AI race. Note also the story recently about OpenAI looking at building their own chips. Companies absolutely care immensely about the cost of GPUs. It's billions of dollars.
janalsncm 3y ago
Special mention to Facebook and Google AI research teams that maintain PyTorch and Tensorflow respectively. And also to ptrblck on the PyTorch forums [1] who has the answer to basically every question it seems. He alone is probably responsible for hundreds of millions of dollars of productivity gain. [1] https://discuss.pytorch.org/u/ptrblck/summary https://discuss.pytorch.org/u/ptrblck/summary

Eisenstein 3y ago

People don't usually get employed to make things with no demand, and people who work for companies with a budget line don't really care how much the nVidia tax is. You can thank hobbyists for creating a lot of demand for compatability with other cards.

kiratp 3y ago
There are so many billions of dollar being spent on this hardware that everyone other than Nvidia is doing everything they can to make competition happen. Eg: https://www.intel.com/content/www/us/en/developer/videos/optimize-dl-workloads-intel-optimized-pytorch.html https://www.intel.com/content/www/us/en/developer/videos/opt... https://www.intel.com/content/www/us/en/developer/tools/oneapi/optimization-for-tensorflow.html https://www.intel.com/content/www/us/en/developer/tools/onea... https://developer.apple.com/metal/tensorflow-plugin/ https://developer.apple.com/metal/tensorflow-plugin/ Large scale opensource is, outside of a few exceptions, built by engineers paid to build it.
roenxi 3y ago
There is huge demand for AMD cards that can efficiently multiply matrices together. The issue is that while there are currently isolated cases where people can make them do that, it doesn't seem to be possible at the scale that it needs to happen at. AMD are being dragged along by the market. Willingly, they aren't fighting it, but their focus has been on other areas.
- viewtransform 3y ago
  <but their focus has been on other areas.> They've shifted a large pool of experienced engineers from legacy software projects to AI and moved the team under a veteran Xilinx AI director. Fingers crossed we should see significant changes in 2024.
  - Flameancer 3y ago
    As a new owner of a 7800XT I’m excited.
- deleted 3y ago
  [deleted]
- iopq 3y ago
  Look at the earnings call: https://www.fool.com/earnings/call-transcripts/2023/08/01/advanced-micro-devices-amd-q2-2023-earnings-call-t/ https://www.fool.com/earnings/call-transcripts/2023/08/01/ad... it's literally ALL AI, server, enterprise talk - AI is mentioned 64 times AMD literally doesn't care about gaming anymore, server is their primary focus
johngossman 3y ago
I can only point you to cloud financial results and the huge cost of the AI race. Note also the story recently about OpenAI looking at building their own chips. Companies absolutely care immensely about the cost of GPUs. It's billions of dollars.

janalsncm 3y ago

Special mention to Facebook and Google AI research teams that maintain PyTorch and Tensorflow respectively. And also to ptrblck on the PyTorch forums [1] who has the answer to basically every question it seems. He alone is probably responsible for hundreds of millions of dollars of productivity gain. [1] https://discuss.pytorch.org/u/ptrblck/summary https://discuss.pytorch.org/u/ptrblck/summary