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Seems odd not to mention competitors such as AMD. Not as if Nvidia has any secret sauce. "Analysts" like this also seem to misunderstand Cuda as that such a s

Seems odd not to mention competitors such as AMD. Not as if Nvidia has any secret sauce. "Analysts" like this also seem to misunderstand Cuda as that such a sauce, when in fact AI wants higher-level toolkits (which are not at all tied to Nvidia).

smoldesu 3y ago
Nvidia does have secret sauce, though. That's kinda the point of CUDA cores and Tensor accelerators and RT cores, even. They are all secret (read: proprietary) and provide relevant "sauce" via performance improvements Nvidia monopolizes. It's arguably unfair, but just the way the cookie crumbles. Nvidia funded GPGPU drivers when AMD and Apple abandoned their cross-platform efforts. Nvidia funded early GAN and transformer-architecture papers, and incorporated their findings into the software layer. Analysts certainly misunderstand CUDA, but that doesn't change it's status as a non-negligible moat. It is a by-product of an overly hostile computer industry, spearheaded by a company that's more than happy to profit from bad blood.
chaostheory 3y ago
CUDA is the secret sauce. AMD has no equivalent, nor does it have a high level alternative. People have begged AMD for this for years and they’ve only started to take it seriously now.
- 0xcde4c3db 3y ago
  AMD's equivalent is HIP [1], for sufficiently flexible definitions of "equivalent". I can't speak to how complete/correct/performant it is (I'm just a guy running tutorial/toy-level ML stuff on an RDNA1 card), but part of AMD's problem is that it might not practically matter how well they do this because the broader ecosystem support specifically for the CUDA stack is so entrenched. [1] https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP
  - chaostheory 3y ago
    I’m not super familiar with HIP, but historically AMD’s past responses to CUDA only supported a small set of GPUs, was poorly documented, and was poorly maintained. It seemed like AMD’s attention span on these projects felt similar to Google’s attention span for new projects ie it’s a good bet that it will die in favor of a new SDK which will ultimately suffer the same fate
    - 0xcde4c3db 3y ago
      I don't disagree with your general impression, but the flip-side of it (combined with the fact that the stack is open-source) is that they're not in a position to execute some exquisitely bullshit market segmentation strategy, and there are at least some wins to be had if you put in a bit of legwork (like making Stable Diffusion run on the GPU I've owned for years instead of spending an indefensible amount of money on the rough Nvidia equivalent).
      - chaostheory 3y ago
        HIP is a good start since it has CUDA compatibility and open source from the beginning. The issue is that developers still might not invest in it until AMD shows that they’re willing to commit to it unlike their past projects. A lot of them have been burned on this front by AMD, but the open source designation should help a lot.
        smoldesu 3y ago
        It's not even AMD's unwillingness to commit. They were among the first to implement Vulkan and OpenCL, and have a decent legacy of GPU hardware support. Their open-source kernel drivers on Linux are cream-of-the-crop, and their raster APIs are so good that they truly rival Nvidia. The biggest issue is frankly that other stakeholders are doing their own thing, and pretending it's okay. Long-term, I think this will be Nvidia's real coup-de-grace; letting local devices do inferencing, while selling their GPUs to larger deployments and training applications. Stuff like CoreML and ROCm are nice, but don't directly threaten CUDA. If their competitors want to be rid of CUDA, then they have to gang up on them and offer a better alternative. Until then, Nvidia will have a market. It's true what they say, nice guys really do finish last in this economy.

smoldesu 3y ago

Nvidia does have secret sauce, though. That's kinda the point of CUDA cores and Tensor accelerators and RT cores, even. They are all secret (read: proprietary) and provide relevant "sauce" via performance improvements Nvidia monopolizes. It's arguably unfair, but just the way the cookie crumbles. Nvidia funded GPGPU drivers when AMD and Apple abandoned their cross-platform efforts. Nvidia funded early GAN and transformer-architecture papers, and incorporated their findings into the software layer. Analysts certainly misunderstand CUDA, but that doesn't change it's status as a non-negligible moat. It is a by-product of an overly hostile computer industry, spearheaded by a company that's more than happy to profit from bad blood.

chaostheory 3y ago

CUDA is the secret sauce. AMD has no equivalent, nor does it have a high level alternative. People have begged AMD for this for years and they’ve only started to take it seriously now.

0xcde4c3db 3y ago
AMD's equivalent is HIP [1], for sufficiently flexible definitions of "equivalent". I can't speak to how complete/correct/performant it is (I'm just a guy running tutorial/toy-level ML stuff on an RDNA1 card), but part of AMD's problem is that it might not practically matter how well they do this because the broader ecosystem support specifically for the CUDA stack is so entrenched. [1] https://github.com/ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP
- chaostheory 3y ago
  I’m not super familiar with HIP, but historically AMD’s past responses to CUDA only supported a small set of GPUs, was poorly documented, and was poorly maintained. It seemed like AMD’s attention span on these projects felt similar to Google’s attention span for new projects ie it’s a good bet that it will die in favor of a new SDK which will ultimately suffer the same fate
  - 0xcde4c3db 3y ago
    I don't disagree with your general impression, but the flip-side of it (combined with the fact that the stack is open-source) is that they're not in a position to execute some exquisitely bullshit market segmentation strategy, and there are at least some wins to be had if you put in a bit of legwork (like making Stable Diffusion run on the GPU I've owned for years instead of spending an indefensible amount of money on the rough Nvidia equivalent).
    - chaostheory 3y ago
      HIP is a good start since it has CUDA compatibility and open source from the beginning. The issue is that developers still might not invest in it until AMD shows that they’re willing to commit to it unlike their past projects. A lot of them have been burned on this front by AMD, but the open source designation should help a lot.
      - smoldesu 3y ago
        It's not even AMD's unwillingness to commit. They were among the first to implement Vulkan and OpenCL, and have a decent legacy of GPU hardware support. Their open-source kernel drivers on Linux are cream-of-the-crop, and their raster APIs are so good that they truly rival Nvidia. The biggest issue is frankly that other stakeholders are doing their own thing, and pretending it's okay. Long-term, I think this will be Nvidia's real coup-de-grace; letting local devices do inferencing, while selling their GPUs to larger deployments and training applications. Stuff like CoreML and ROCm are nice, but don't directly threaten CUDA. If their competitors want to be rid of CUDA, then they have to gang up on them and offer a better alternative. Until then, Nvidia will have a market. It's true what they say, nice guys really do finish last in this economy.