Since you’re on Linux, it’s just a matter of installing the right packages from your distros package manager. Lots of articles on the Web, just google your app + “ROCm”. Main thing you gotta keep in mind is the version dependencies, since ROCm 6.0/6.1 was released recently, some programs may not yet have been updated for it. So if your distro packages the most recent version, your app might not yet support it.
This is why many ML apps also come as a Docker image with specific versions of libraries bundled with them - so that could be an easier option for you, instead of manually hunting around for various package dependencies.
Also, chances are that your app may not even know/care about ROCm, if it just uses a library like PyTorch / TensorFlow etc. So just check it’s requirements first.
As for AMD vs nVidia in general, there are a few places mainly where they lagged behind: RTX, compute and super sampling.
For RTX, there has been improvements in performance with the RDNA3 cards, but it does lag behind by a generation. For instance, the latest 7900 XTX’s RTX performance is equivalent to the 3080.
Compute is catching up as I mentioned earlier, and in some cases the performance may even match nVidia. This is very application/library specific though, so you’ll need to look it up.
Super Sampling is a bit of a weird one. AMD has FSR and it does a good job in general. In some cases, it may even perform better since it uses much simpler calculations, as opposed to nVidia’s deep learning technique. And AMD’s FSR method can be used with any card in fact, as long as the game supports it. And therein lies the catch, only something like 1/3rd of the games out there support it, and even fewer games support the latest FSR 3. But there are mods out there which can enable FSR (check Nexus Mods) that you might be able to use. In any case, FSR/DLSS isn’t a critical thing, unless you’re gaming on a 4K+ monitor.
You can check out Tom’s Hardware GPU Hierarchy for the exact numbers - scroll down halfway to read about the RTX and FSR situation.
So yes, AMD does lag behind in nVidia but whether this impacts you really depends on your needs and use cases. If you’re a Linux user though, getting an AMD is a no-brainer - it just works so much better, as in, no need to deal with proprietary driver headaches, no update woes, excellent Wayland support etc.
Yes, I am running NixOS with Hyprland at the moment as a trial and most things were pretty well. I know that open source NVIDIA drivers are crap especially if you want to run Wayland, but I am more interested into the AI/ML side as I want to play a bit with open weight LLMs, and Pytorch. I used to do some AI with Tensorflow, but I would like to learn more about Pytorch.
I used to have an older AMD card in the past that I borrowed from a friend and tried to install Rocm and it was an absolute disaster. That was around COVID and even though I consider myself fairly familiar with Linux and very comfortable around the command line, I didn’t make it work back then.
The majority of the opinions I have also read were just pointing out that CUDA is just plug and play and Rocm is a lot of tinkering. And I think I am simply too old and tired of this constant tinkering and I would prefer something that will simply just work out of the box.
I really hate NVIDIA and don’t like the company but still consider them with something like i3, just to have some peace of mind and know that everything works out of the box with their proprietary drivers.
Since you’re on Linux, it’s just a matter of installing the right packages from your distros package manager. Lots of articles on the Web, just google your app + “ROCm”. Main thing you gotta keep in mind is the version dependencies, since ROCm 6.0/6.1 was released recently, some programs may not yet have been updated for it. So if your distro packages the most recent version, your app might not yet support it.
This is why many ML apps also come as a Docker image with specific versions of libraries bundled with them - so that could be an easier option for you, instead of manually hunting around for various package dependencies.
Also, chances are that your app may not even know/care about ROCm, if it just uses a library like PyTorch / TensorFlow etc. So just check it’s requirements first.
As for AMD vs nVidia in general, there are a few places mainly where they lagged behind: RTX, compute and super sampling.
For RTX, there has been improvements in performance with the RDNA3 cards, but it does lag behind by a generation. For instance, the latest 7900 XTX’s RTX performance is equivalent to the 3080.
Compute is catching up as I mentioned earlier, and in some cases the performance may even match nVidia. This is very application/library specific though, so you’ll need to look it up.
Super Sampling is a bit of a weird one. AMD has FSR and it does a good job in general. In some cases, it may even perform better since it uses much simpler calculations, as opposed to nVidia’s deep learning technique. And AMD’s FSR method can be used with any card in fact, as long as the game supports it. And therein lies the catch, only something like 1/3rd of the games out there support it, and even fewer games support the latest FSR 3. But there are mods out there which can enable FSR (check Nexus Mods) that you might be able to use. In any case, FSR/DLSS isn’t a critical thing, unless you’re gaming on a 4K+ monitor.
You can check out Tom’s Hardware GPU Hierarchy for the exact numbers - scroll down halfway to read about the RTX and FSR situation.
So yes, AMD does lag behind in nVidia but whether this impacts you really depends on your needs and use cases. If you’re a Linux user though, getting an AMD is a no-brainer - it just works so much better, as in, no need to deal with proprietary driver headaches, no update woes, excellent Wayland support etc.
Yes, I am running NixOS with Hyprland at the moment as a trial and most things were pretty well. I know that open source NVIDIA drivers are crap especially if you want to run Wayland, but I am more interested into the AI/ML side as I want to play a bit with open weight LLMs, and Pytorch. I used to do some AI with Tensorflow, but I would like to learn more about Pytorch.
I used to have an older AMD card in the past that I borrowed from a friend and tried to install Rocm and it was an absolute disaster. That was around COVID and even though I consider myself fairly familiar with Linux and very comfortable around the command line, I didn’t make it work back then.
The majority of the opinions I have also read were just pointing out that CUDA is just plug and play and Rocm is a lot of tinkering. And I think I am simply too old and tired of this constant tinkering and I would prefer something that will simply just work out of the box.
I really hate NVIDIA and don’t like the company but still consider them with something like i3, just to have some peace of mind and know that everything works out of the box with their proprietary drivers.
Since you run NixOS, these things might be helpful for you:
https://nixos.wiki/wiki/AMD_GPU#HIP
https://github.com/nixos-rocm/nixos-rocm