5 ms·

It's time to upscale FSR 2 even further: Meet FSR 2.1

60 points by WithinReason 4y ago

WithinReason 4y ago
Wonder how long AMD's hand crafted upscalers can keep up with Nvidia's neural networks. Probably not long, and AMD is adding a tensor core equivalent in their next gen: https://videocardz.com/newz/amd-adds-wmma-wave-matrix-multiply-accumulate-support-to-gfx11-rdna3-architecture-amds-tensor-core https://videocardz.com/newz/amd-adds-wmma-wave-matrix-multip...
- izacus 4y ago
  They already can't keep up (DLSS has always been better), but not having to buy a new nVidia core for FSR is a massive selling point for the technology. Even Steam Deck supports it.
  - cypress66 4y ago
    Steam deck supports FSR 1, which is basically unrelated to FSR 2 and DLSS.
    - jerojero 4y ago
      Steam deck also supports FSR2 just not globally like it does FSR1. For FSR2 you need game-specific data so it's dependent on the game developers to adopt it.
  - m-p-3 4y ago
    It can even be implemented in consoles version of the game.
  - highwind 4y ago
    I don't know which game implements it but the Nintendo Switch's system license lists FSR as one of the libraries that it uses. So I guess even Nintendo Switch supports it.
  - hajile 4y ago
    What is the cost (in both time and money) of having to run every patch and update through Nvidia's upscaling service?
    - ZekeSulastin 4y ago
      That isn’t a thing in DLSS 2.
ParksNet 4y ago
Looking forward to the Digital Foundry video.
monkmartinez 4y ago
How do AMD gpus compare with Nvidia for deeplearning workloads? Configuration is tough enough on your own hardware with Nvidia. I have zero experience with AMD cards and suspect the challenge would be greater. I do note that I saw support for AMD pretty early on last week when Stable-Diffusion was released. Nvidia may pull some pricing shenanigans for the rest of 2022 and into 2023 according to this video[1] I watched. I would be thrilled to see some real competition for workloads that are not gaming. Nvidia just dominates so far as I can tell. [1] https://www.youtube.com/watch?v=15FX4pez1dw https://www.youtube.com/watch?v=15FX4pez1dw
- nolist_policy 4y ago
  Very easy actually. This is not officially documented, but with a recent enough kernel you don't have to install anything. You can grab the official rocm container and it'll just work. For example for Stable Diffusion see https://github.com/AshleyYakeley/stable-diffusion-rocm/blob/master/rocm/Dockerfile https://github.com/AshleyYakeley/stable-diffusion-rocm/blob/...
- deleted 4y ago
  [deleted]
- cercatrova 4y ago
  Just don't bother with AMD if you want wide support for deep learning. While it is getting better, CUDA is king and will remain so for the future. I had an AMD GPU and I gave up and bought an Nvidia one for ML workloads. The gaming features are also superior to AMD's equivalents as well, like RTX and DLSS.
  - nextaccountic 4y ago
    I just don't want to support nvidia :(
  - black_puppydog 4y ago
    Out of interest, when did you try this? I just set up pytorch on a machine the other day and while I don't own an AMD GPU I saw that they offer a ROCm version on their "getting started" page [1] and it doesn't seem to be more difficult than the other options. I could imagine if you run other frameworks, or if you compile custom (c++) modules for pytorch then it could be an issue, but frankly that has been becoming exceedingly rare (at least for me). Would love to hear your or anyone's experiences since I, like the sibling comment, don't want to shove any more money to nvidia. [1]: https://pytorch.org/get-started/locally/ https://pytorch.org/get-started/locally/

60 points by WithinReason 4y ago

Wonder how long AMD's hand crafted upscalers can keep up with Nvidia's neural networks. Probably not long, and AMD is adding a tensor core equivalent in their next gen: https://videocardz.com/newz/amd-adds-wmma-wave-matrix-multiply-accumulate-support-to-gfx11-rdna3-architecture-amds-tensor-core https://videocardz.com/newz/amd-adds-wmma-wave-matrix-multip...

izacus 4y ago
They already can't keep up (DLSS has always been better), but not having to buy a new nVidia core for FSR is a massive selling point for the technology. Even Steam Deck supports it.
- cypress66 4y ago
  Steam deck supports FSR 1, which is basically unrelated to FSR 2 and DLSS.
  - jerojero 4y ago
    Steam deck also supports FSR2 just not globally like it does FSR1. For FSR2 you need game-specific data so it's dependent on the game developers to adopt it.
- m-p-3 4y ago
  It can even be implemented in consoles version of the game.
- highwind 4y ago
  I don't know which game implements it but the Nintendo Switch's system license lists FSR as one of the libraries that it uses. So I guess even Nintendo Switch supports it.
- hajile 4y ago
  What is the cost (in both time and money) of having to run every patch and update through Nvidia's upscaling service?
  - ZekeSulastin 4y ago
    That isn’t a thing in DLSS 2.

ParksNet 4y ago

Looking forward to the Digital Foundry video.

monkmartinez 4y ago

How do AMD gpus compare with Nvidia for deeplearning workloads? Configuration is tough enough on your own hardware with Nvidia. I have zero experience with AMD cards and suspect the challenge would be greater. I do note that I saw support for AMD pretty early on last week when Stable-Diffusion was released. Nvidia may pull some pricing shenanigans for the rest of 2022 and into 2023 according to this video[1] I watched. I would be thrilled to see some real competition for workloads that are not gaming. Nvidia just dominates so far as I can tell. [1] https://www.youtube.com/watch?v=15FX4pez1dw https://www.youtube.com/watch?v=15FX4pez1dw

nolist_policy 4y ago
Very easy actually. This is not officially documented, but with a recent enough kernel you don't have to install anything. You can grab the official rocm container and it'll just work. For example for Stable Diffusion see https://github.com/AshleyYakeley/stable-diffusion-rocm/blob/master/rocm/Dockerfile https://github.com/AshleyYakeley/stable-diffusion-rocm/blob/...
deleted 4y ago
[deleted]
cercatrova 4y ago
Just don't bother with AMD if you want wide support for deep learning. While it is getting better, CUDA is king and will remain so for the future. I had an AMD GPU and I gave up and bought an Nvidia one for ML workloads. The gaming features are also superior to AMD's equivalents as well, like RTX and DLSS.
- nextaccountic 4y ago
  I just don't want to support nvidia :(
- black_puppydog 4y ago
  Out of interest, when did you try this? I just set up pytorch on a machine the other day and while I don't own an AMD GPU I saw that they offer a ROCm version on their "getting started" page [1] and it doesn't seem to be more difficult than the other options. I could imagine if you run other frameworks, or if you compile custom (c++) modules for pytorch then it could be an issue, but frankly that has been becoming exceedingly rare (at least for me). Would love to hear your or anyone's experiences since I, like the sibling comment, don't want to shove any more money to nvidia. [1]: https://pytorch.org/get-started/locally/ https://pytorch.org/get-started/locally/