Hi, this is actually pretty important for my business. I've been waiting for the driver to be released for a year now. We got the binary a few months ago under NDA, but open sourcing it, is next level for us.
The pro vii (a wonderfully spec’d card for my purposes) had a bunch of awesome features-even remote access-but then they pulled the marketing pages from their website and stopped shipping the link bridge (non mpx) to water down the value of the card. I have no idea how AMD works.
It blows my mind how reliably AMD shoots itself in the foot. What we want isn’t that hard:
1) Support your graphics cards on linux using kernel drivers that you upstream. All of them. Not just a handful - all the ones you sell from say 18 months ago till today.
2) Make GPU acceleration actually work out of the box for pytorch and tensorflow. Not some special fork, patched version that you “maintain” on your website, the tip of the main branch for both of those libraries should just compile out of the box and give people gpu-accelerated ML.
This is table stakes but it blows my mind that they keep making press releases and promises like this that things are on the roadmap without doing thing one and unfucking the basic dev experience so people can actually use their GPUs for real work.
How it actually is:
1) Some cards work with rocm, some cards work with one of the other variations of BS libraries they have come up with over the years. Some cards work with amdgpu but many only work with proprietary kernel drivers which means if you don’t use precisely one of the distributions and kernel versions that they maintain you are sool.
2) Nothing whatsoever builds out of the box and when you get it to build almost nothing runs gpu accelerated. For me, pytorch requires a special downgrade, a python downgrade and a switch to a fork that AMD supposedly maintain although it doesn’t compile for me and when I managed to beat it into a shape where it compiled it wouldn’t run GPU accelerated even though games use the GPU just fine. I have a GPU that is supposedly current, so they are actively selling it, but can I use it? Can I bollocks. Ollama won’t talk to my GPU even though it supposedly works with ROCm. It only works with ROCm with some graphics cards. Tensorflow similar story when I last tried it although admittedly I didn’t try as hard as pytorch.
Just make your shit work so that people can use it. It really shouldn’t be that hard. The dev experience with NVidia is a million times better.
It doesn't diminish most of your points, but getting PyTorch to work on Arch Linux is as easy as installing the `python-pytorch-opt-rocm` package. Similar with Ollama: `ollama-rocm`. So if you just want to use PyTorch, and don't need the very latest version, I wouldn't say the dev experience with Nvidia is much better.
Same experience here. Installing ROCm and Ollama on my box were both dead simple and everything worked right out of the box. Using an RX 7900 XTX card, FWIW.
Meanwhile NVidia has embraced Python as first class programming language on CUDA, with the new cuTile format, as companion to PTX.
And given the tooling they are adding full steam ahead for Python, I even wonder if Mojo will manage to get enough mindshare, let alone what AMD and Intel are not doing.
Sucks that you've had so much trouble... My experience with my cheap 6750XT is that it just works OOTB on Arch with rocm, llama.cpp, ollama, whisper, etc by setting an envvar.
Adding to the list above. Please, PLEASE, give me a single place I can look to see what pytorch will actually support. I would probably buy something if I could get a straight answer on what will/won't actually work.
AMD has two driver teams at this point. One of Linux/Open Source, one for Catalyst/Closed source, and they are not allowed to interact.
Because, there are tons of IP and trade secrets involved in driver development and optimization. Sometimes game related, sometimes for patching a rogue application which developers can't or don't fix, etc. etc.
GPU drivers are ought to be easy, but in reality, they are not. The open source drivers are "vanilla" drivers without all these case-dependent patching and optimization. Actually, they really work well out of the box for normal desktop applications. I don't think there are any cards which do (or will) not work with the open source kernel drivers as long as you use a sufficiently recent version.
...and you mention ROCm.
I'm not sure how ROCm's intellectual underpinnings are but, claiming lack of effort is a bit unfair to AMD. Yes, software was never their strong suit, but they're way better when compared to 20 years earlier. They have a proper open source driver which works, and a whole fleet of open source ROCm packages, which is very rigorously CI/CD tested by their maintainers now.
Do not forget that some of the world's most powerful supercomputers run on Instinct cards, and AMD is getting tons of experience from these big players. If you think the underpinnings of GPGPU libraries are easy, I can only say that the reality is very different. The simple things people do with PyTorch and other very high level libraries pull enormous tricks under the hood, and you're really pushing the boundaries of the hardware performance and capability-wise.
NVIDIA is not selling a tray full of switches and GPUs and require OEMs to integrate it as-is for no reason. On the other hand, the same NVIDIA acts very slowly to enable an open source ecosystem.
So, yes, AMD is not in an ideal position right now, but calling them incompetent doesn't help either.
P.S.: The company which fought for a completely open source HDMI 2.1 capable display driver is AMD, not NVIDIA.
I accept that there are two teams for reasons that include IP. However, Nvidia must have the same problem and they appear not to be hamstrung by it. So what is the difference?
NVIDIA and AMD, from my experience, have completely different cultures.
ATI started as a much more closed company. Then, they pivoted and started to open their parts. They were hamstrung at the HDCP at one point, and they decided to decouple HDCP block from video accelerators at silicon level to allow open source drivers to access video hardware without leaking/disabling HDCP support. So, they devoted to open what they have, but when you have tons of legacy IP, things doesn't go from 0 to 100 in a day. I want to remind that "game dependent driver optimization" started pre 2000s. This is how rooted these codebases are.
NVIDIA took a different approach, which was being indifferent on the surface, but their hardware became a bit more hostile at every turn towards nouveau. Then they released some specs to allow "hardware enablement" by nouveau, so closed source drivers can be installed, and the card didn't blank-screened at boot.
Then, as they fought with kernel developers, with some hard prodding by Kernel guys and some coercing by RedHat, NVIDIA accepted to completely remove "shim" shenanigans, and moved closed bits of the kernel module to card firmware by revising card architecture. It's important to keep in mind that NVIDIA's open drivers means "an open, bare bones kernel module, a full fledged and signed proprietary firmware which can be used by closed source drivers only and a closed source GLX stack", where in AMD this means "An open source kernel module, standard MESA libraries, and a closed source firmware available to all drivers".
It was in talks with nouveau guys to allow them to use the full powered firmware with clock and power management support, but I don't know where it went.
The CUDA environment is also the same. Yes it works very well, and it's a vast garden, but it's walled and protected by electrified fence and turrets. You're all in, or all out.
I'm wondering how much effort went into RE Nvidia cards and drivers. Graphics card drivers are completely a mythical beast to me, and I guess it's one of the most complicated drivers in the hardware world.
Fact of the matter is that I have a Radeon RX 6600, which I can't use with ollama. First, there is no ROCm at all in my distros repository - it doesn't compile reliably and needs too many ressources. Then, when compiling it manually, it turns out that ROCm doesn't even support the card in the first place.
I'm aware that 8GB Vram are not enough for most such workloads. But no support at all? That's ridiculous. Let me use the card and fall back to system memory for all I care.
Nvidia, as much as I hate their usually awfully insufficient linux support, has no such restrictions for any of their modern cards, as far as I'm aware.
My recent experience has been that the Vulkan support in llama.cpp is pretty good. It may lag behind Cuda / Metal for the bleeding edge models if they need a new operator.
You should be able to use ollama’s Vulkan backend and in my experience the speed will be the same. (I just spent a bunch of time putting Linux on my 2025 ASUS ROG Flow Z13 to use ROCm, only to see the exact same performance as Vulkan.)
> I'm aware that 8GB Vram are not enough for most such workloads. But no support at all? That's ridiculous. Let me use the card and fall back to system memory for all I care.
> Nvidia, as much as I hate their usually awfully insufficient linux support, has no such restrictions for any of their modern cards, as far as I'm aware.
In fact, I regularly run llamafile (and sometimes ollama) on an nvidia dGPU in a laptop, with 4GB of VRAM, and it works fine (ish... I mostly do the thing where some layers are on the GPU and some are CPU; it's still faster than pure CPU so whatever).
A laundry list of excuses ... or a list of things to work on. ("Why the hell do we have two driver teams?" - would be my #1 thing to fix if I was at AMD.)
The "fix" would be to make games perform like shit on Windows and disable HDR and other proprietary features, or to abolish the open Linux drivers. You can't have both, unless you do what Nvidia does and move all of the proprietary stuff to the GPU firmware and write a minimal driver to control that massive firmware blob. Which, obviously, would require reengineering the GPU hardware, which is expensive and of questionable value.
They can't open source their proprietary drivers even if they wanted to because they don't own all of the IP and their code is full of NDA'd trade secrets. AMD isn't paying two different teams to do the same work because they like wasting money.
NVIDIA's blobs are different when compared to others. They do not want to give away how their GPU clocking and enablement works. As a result, NVIDIA's blobs are both signed and picky about "who" they communicate with. You can't use the NVIDIA's full fledged firmware with nouveau for example.
On the other hand, the card enablement sequences are open for AMD and Intel. AMD only protects card's thermal and fan configuration data to preventing card damage, AFAIK. You can clock the card and use its power management features the way you like. For NVIDIA, even they are out of reach.
AMD's open drivers work way better than NVIDIA's closed ones, too. I have never seen how a single application refused to launch until I used NVIDIA closed drivers.
I guess that you don't understand that how silicon and 3rd party IP works. It took Intel a completely new GPU from scratch to be able to open drivers. AMD did at least one revision to their silicon to enable that kind of openness.
Yet, HDMI forum said that they can't implement an HDMI2.1 capable driver in the open, with some nasty legal letters.
I have a couple of friends who wrote 3D engines from scratch and debugged graphics drivers for their engines for a living. It's a completely different jungle filled with completely different beasts.
I think being able to call glxinfo on an AMD card running with completely open drivers and being able to see extensions from NVIDIA, AMD, SGI, IBM and others is a big win already.
> Support your graphics cards on linux using kernel drivers that you upstream. All of them. Not just a handful - all the ones you sell from say 18 months ago till today.
All the stuff works even if it's not officially supported. It's not that hard to set a single environment variable (HSA_OVERRIDE_GFX_VERSION).
Like literally, everything works, from Vega 56/64 to ryzen 99xx iGPUs.
Also, try nixos. Everything literally works with a single config entry after recent merge of rocm 6.3. I successfully run a zoo of various radeons of different generations.
But why do you even have to do this fucking bullshit that you randomly stumble upon, while googling error message after error message, ending up in random github repos and issues?
And no, just because the three random cards you have work doesn't mean "everything works". Just tried an MI300A a few months ago... I just wanted to test ollama as this is one of the hottest applications for GPU acceleration now, it will surely be well supported right? First, the gfx version listed for it in the ollama docs is wrong - but OK, figured it out. Then Tried some random models with it, the only output it ever generates is GGGGGGGGGGGGG. Apparently only fp16 models work, nothing more quantized. So I pick one explicitly. Then it's slower than running on the cpu in the same system.
Thanks but no thanks; this cost me two days when Nvidia just works first try.
IIRC there was only one AMD employee that was working to integrate linux based things. Often, the response was - things are stuck in Intellectual property, or project managers etc. So even specs were not available.
SemiAnalysis had a good article on this recently, basically the reason AMD still sucks on the ML software side is that their compensation for devs is significantly worse than competitors like NVidia, Google and OpenAI, so most of the most competent devs go elsewhere.
Anyone else find it funny that AMD releases new GPU features while people still can't get basic ML stuff working? It's like building a fancy garage before fixing the broken car.
Maybe ROCm is a bit of a mess, but AMD makes amazing graphics cards.
And they open sourced their GPU driver, which is a massive plus in my book.
If Radeon got virtualization support, it would make it the perfect GPU for running video games (or other GPU applications) in virtual machines - you wouldn't need to mess with PCIe passthrough anymore.
What I wrote about this on twitter: https://x.com/HotAisle/status/1914549886185611627
https://open-iov.org/index.php/GPU_Support#AMD
https://github.com/GPUOpen-LibrariesAndSDKs/MxGPU-Virtualiza...
As "AI" use cases mature, NPU/AIE-ML virtualization will also be needed.
https://news.ycombinator.com/item?id=43780972
Yeah, not gonna read that.
1) Support your graphics cards on linux using kernel drivers that you upstream. All of them. Not just a handful - all the ones you sell from say 18 months ago till today.
2) Make GPU acceleration actually work out of the box for pytorch and tensorflow. Not some special fork, patched version that you “maintain” on your website, the tip of the main branch for both of those libraries should just compile out of the box and give people gpu-accelerated ML.
This is table stakes but it blows my mind that they keep making press releases and promises like this that things are on the roadmap without doing thing one and unfucking the basic dev experience so people can actually use their GPUs for real work.
How it actually is: 1) Some cards work with rocm, some cards work with one of the other variations of BS libraries they have come up with over the years. Some cards work with amdgpu but many only work with proprietary kernel drivers which means if you don’t use precisely one of the distributions and kernel versions that they maintain you are sool.
2) Nothing whatsoever builds out of the box and when you get it to build almost nothing runs gpu accelerated. For me, pytorch requires a special downgrade, a python downgrade and a switch to a fork that AMD supposedly maintain although it doesn’t compile for me and when I managed to beat it into a shape where it compiled it wouldn’t run GPU accelerated even though games use the GPU just fine. I have a GPU that is supposedly current, so they are actively selling it, but can I use it? Can I bollocks. Ollama won’t talk to my GPU even though it supposedly works with ROCm. It only works with ROCm with some graphics cards. Tensorflow similar story when I last tried it although admittedly I didn’t try as hard as pytorch.
Just make your shit work so that people can use it. It really shouldn’t be that hard. The dev experience with NVidia is a million times better.
Same experience here. Installing ROCm and Ollama on my box were both dead simple and everything worked right out of the box. Using an RX 7900 XTX card, FWIW.
And given the tooling they are adding full steam ahead for Python, I even wonder if Mojo will manage to get enough mindshare, let alone what AMD and Intel are not doing.
Because, there are tons of IP and trade secrets involved in driver development and optimization. Sometimes game related, sometimes for patching a rogue application which developers can't or don't fix, etc. etc.
GPU drivers are ought to be easy, but in reality, they are not. The open source drivers are "vanilla" drivers without all these case-dependent patching and optimization. Actually, they really work well out of the box for normal desktop applications. I don't think there are any cards which do (or will) not work with the open source kernel drivers as long as you use a sufficiently recent version.
...and you mention ROCm.
I'm not sure how ROCm's intellectual underpinnings are but, claiming lack of effort is a bit unfair to AMD. Yes, software was never their strong suit, but they're way better when compared to 20 years earlier. They have a proper open source driver which works, and a whole fleet of open source ROCm packages, which is very rigorously CI/CD tested by their maintainers now.
Do not forget that some of the world's most powerful supercomputers run on Instinct cards, and AMD is getting tons of experience from these big players. If you think the underpinnings of GPGPU libraries are easy, I can only say that the reality is very different. The simple things people do with PyTorch and other very high level libraries pull enormous tricks under the hood, and you're really pushing the boundaries of the hardware performance and capability-wise.
NVIDIA is not selling a tray full of switches and GPUs and require OEMs to integrate it as-is for no reason. On the other hand, the same NVIDIA acts very slowly to enable an open source ecosystem.
So, yes, AMD is not in an ideal position right now, but calling them incompetent doesn't help either.
P.S.: The company which fought for a completely open source HDMI 2.1 capable display driver is AMD, not NVIDIA.
ATI started as a much more closed company. Then, they pivoted and started to open their parts. They were hamstrung at the HDCP at one point, and they decided to decouple HDCP block from video accelerators at silicon level to allow open source drivers to access video hardware without leaking/disabling HDCP support. So, they devoted to open what they have, but when you have tons of legacy IP, things doesn't go from 0 to 100 in a day. I want to remind that "game dependent driver optimization" started pre 2000s. This is how rooted these codebases are.
NVIDIA took a different approach, which was being indifferent on the surface, but their hardware became a bit more hostile at every turn towards nouveau. Then they released some specs to allow "hardware enablement" by nouveau, so closed source drivers can be installed, and the card didn't blank-screened at boot.
Then, as they fought with kernel developers, with some hard prodding by Kernel guys and some coercing by RedHat, NVIDIA accepted to completely remove "shim" shenanigans, and moved closed bits of the kernel module to card firmware by revising card architecture. It's important to keep in mind that NVIDIA's open drivers means "an open, bare bones kernel module, a full fledged and signed proprietary firmware which can be used by closed source drivers only and a closed source GLX stack", where in AMD this means "An open source kernel module, standard MESA libraries, and a closed source firmware available to all drivers".
It was in talks with nouveau guys to allow them to use the full powered firmware with clock and power management support, but I don't know where it went.
The CUDA environment is also the same. Yes it works very well, and it's a vast garden, but it's walled and protected by electrified fence and turrets. You're all in, or all out.
Probably because they don't have an open-source driver for linux and they can focus on the proprietary one.
I'm aware that 8GB Vram are not enough for most such workloads. But no support at all? That's ridiculous. Let me use the card and fall back to system memory for all I care.
Nvidia, as much as I hate their usually awfully insufficient linux support, has no such restrictions for any of their modern cards, as far as I'm aware.
Try it out! Benchmarks here: https://github.com/ggml-org/llama.cpp/discussions/10879
(ollama doesn’t support vulkan for some weird reason. I guess they never pulled the code from llama.cpp)
> Nvidia, as much as I hate their usually awfully insufficient linux support, has no such restrictions for any of their modern cards, as far as I'm aware.
In fact, I regularly run llamafile (and sometimes ollama) on an nvidia dGPU in a laptop, with 4GB of VRAM, and it works fine (ish... I mostly do the thing where some layers are on the GPU and some are CPU; it's still faster than pure CPU so whatever).
They can't open source their proprietary drivers even if they wanted to because they don't own all of the IP and their code is full of NDA'd trade secrets. AMD isn't paying two different teams to do the same work because they like wasting money.
On the other hand, the card enablement sequences are open for AMD and Intel. AMD only protects card's thermal and fan configuration data to preventing card damage, AFAIK. You can clock the card and use its power management features the way you like. For NVIDIA, even they are out of reach.
AMD's open drivers work way better than NVIDIA's closed ones, too. I have never seen how a single application refused to launch until I used NVIDIA closed drivers.
Yet, HDMI forum said that they can't implement an HDMI2.1 capable driver in the open, with some nasty legal letters.
I have a couple of friends who wrote 3D engines from scratch and debugged graphics drivers for their engines for a living. It's a completely different jungle filled with completely different beasts.
I think being able to call glxinfo on an AMD card running with completely open drivers and being able to see extensions from NVIDIA, AMD, SGI, IBM and others is a big win already.
What does the 3d engine look like? Custom made AAA in companies such as EA or Ubisoft?
All the stuff works even if it's not officially supported. It's not that hard to set a single environment variable (HSA_OVERRIDE_GFX_VERSION).
Like literally, everything works, from Vega 56/64 to ryzen 99xx iGPUs.
Also, try nixos. Everything literally works with a single config entry after recent merge of rocm 6.3. I successfully run a zoo of various radeons of different generations.
And no, just because the three random cards you have work doesn't mean "everything works". Just tried an MI300A a few months ago... I just wanted to test ollama as this is one of the hottest applications for GPU acceleration now, it will surely be well supported right? First, the gfx version listed for it in the ollama docs is wrong - but OK, figured it out. Then Tried some random models with it, the only output it ever generates is GGGGGGGGGGGGG. Apparently only fp16 models work, nothing more quantized. So I pick one explicitly. Then it's slower than running on the cpu in the same system.
Thanks but no thanks; this cost me two days when Nvidia just works first try.
And they open sourced their GPU driver, which is a massive plus in my book.
If Radeon got virtualization support, it would make it the perfect GPU for running video games (or other GPU applications) in virtual machines - you wouldn't need to mess with PCIe passthrough anymore.