Just spent the last week or so porting TheRock to stagex in an effort to get ROCm built with a native musl/mimalloc toolchain and get it deterministic for high security/privacy workloads that cannot trust binaries only built with a single compiler.
It has been a bit of a nightmare and had to package like 30+ deps and their heavily customized LLVM, but got the runtime to build this morning finally.
Things are looking bright for high security workloads on AMD hardware due to them working fully in the open however much of a mess it may be.
I also attempted to package ROCM on musl. Specifically, packaging it for Alpine Linux.
It truly is a nightmare to build the whole thing. I got past the custom LLVM fork and a dozen other packages, but eventually decided it had been too much of a time sink.
I’m using llama.cpp with its vulkan support and it’s good enough for my uses. Vulkan so already there and just works. It’s probably on your host too, since so many other things rely on it anyway.
That said, I’d be curious to look at your build recipes. Maybe it can help power through the last bits of the Alpine port.
AMD has years of catching up to do with ROCm just to get their devices to work well. They don't support all their own graphics cards that can do AI, and when it is supported, it's buggy. The AMDGPU graphics driver for Linux has had continued instability since 6.6. I don't understand why they can't hire better software engineers.
In my experience fiddling with compute shaders a long time ago, cuda and rocm and opencv are way too much hassle to set up. Usually it takes a few hours to get the toolkits and SDK up and running that is, if you CAN get it up and running. The dependencies are way too big as well, cuda is 11gb??? Either way, just use Vulkan. Vulkan "just works" and doesn't lock you into Nvidia/amd.
Vulkan is a pain for different reasons. Easier to install sure, but you need a few hundred lines of code to set up shader compilation and resources, and you’ll need extensions to deal with GPU addresses like you can with CUDA.
> Challenger AMD’s ability to take data center GPU share from market leader Nvidia will certainly depend on the success or failure of its AI software stack, ROCm.
I don't think this is true. ROCm is a huge advantage for Nvidia but as far as I can tell it is more a set of R&D libraries than anything else, so all the Hot New Stuff keeps being Nvidia first and only (to start with) as the library ecosystem for the hotness doesn't exist yet. Then eventually new libraries are created that are CUDA independent and AMD turns out to make pretty good graphics cards.
I wouldn't be surprised of ROCm withered on the vine and AMD still does fine.
I've been using ROCm on my Radeon RX 6800 and my Ryzen AI 7 350 systems. I've only used it for GPU-accelerated rendering in Cycles, but I am glad that AMD has an option that isn't OpenCL now.
Someone from AMD posted this a few minutes ago, then deleted it:
"Anush's success is due to opting out of internal bureaucracy than anything else. most Claude use at AMD goes through internal infrastructure that can take hundreds of seconds per response due to throttling. Anush got us an exemption to use Anthropic directly. he is also exempt from normal policies on open source and so I can directly contribute to projects to add AMD support. He's an effective leader and has turned ROCm into a internal startup based in California. Definitely worth joining the team even if you've heard bad things about AMD as a whole."
This kind of bullshit is why I don't want to join AMD, even if this particular team is temporarily exempt from it.
> he is also exempt from normal policies on open source and so I can directly contribute to projects to add AMD support.
It's crazy that this is a big deal.
I understand the need for some kind of governance around this but for it to require a special exemption just shows how far the AMD culture needs to shift.
AMD hasn't signaled in behavior or words that they're going to actually support ROCm on $specificdevice for more than 4-5 years after release. Sometimes it's as little as the high 3.x years for shrinks like the consumer AMD RX 580. And often the ROCm support for consumer devices isn't out until a year after release, further cutting into that window.
Meanwhile nvidia just dropped CUDA/driver support for 1xxx series cards from their most recent drivers this year.
Is it really that short? This support matrix shows ROCm 7.2.1 supporting quite old generations of GPUs, going back at least five or six years. I consider longevity important, too, but if they're actively supporting stuff released in 2020 (CDNA), I can't fault them too much. With open drivers on Linux, where all the real AI work is happening, I feel like this is a better longevity story than nvidia...where you're dependent on nvidia for kernel drivers in addition to CUDA.
Last year, AMD ran a GitHub poll for ROCm complaints and received more than 1,000 responses. Many were around supporting older hardware, which is today supported either by AMD or by the community, and one year on, all 1,000 complaints have been addressed, Elangovan said. AMD has a team going through GitHub complaints, but Elangovan continues to encourage developers to reach out on X where he’s always happy to listen.
Seems like they're making some effort in that direction at least. If you have specific concerns, maybe try hitting up Anush Elangovan on Twitter?
ROCm is open source and TheRock is community maintained, and in a minute the first Linux distro will have native in-tree builds. It will be supported for the foreseeable future due to AMDs open development approach.
It is Nvidia that has the track record of closed drivers and insisting on doing all software dev without community improvements to expected results.
I was thinking to get 2x r9700 for a home workstation (mostly inference). It is much cheaper than a similar nvidia build. But still not sure if good value or more trouble.
I own a single R9700 for the same reason you mentioned, looking into getting a second one. Was a lot of fiddling to get working on arch but RDNA4 and ROCm have come a long way. Every once in a while arch package updates break things but that’s not exclusive to ROCm.
LLM’s run great on it, it’s happily running gemma4 31b at the moment and I’m quite impressed. For the amount of VRAM you get it’s hard to beat, apart from the Intel cards maybe. But the driver support doesn’t seem to be that great there either.
Had some trouble with running comfyui, but it’s not my main use case, so I did not spent a lot of time figuring that out yet
Talking to friends who have fought more homelab battles than I ever will, my sense is that (1) AMD has done a better job with RDNA4 than the past generations, and (2) it seems very workload-dependent whether AMD consumer gear is "good value", "more trouble", or both at the same time.
Edit: I misread the "2x r9700" as "2 rx9700" which differs from the topic of this comment (about RNDA4 consumer SKUs). I'll keep my comment up, but anyone looking to get Radeon PRO cards can (should?) disregard.
Apple got it right with unified memory with wide bus. That's why Mac Minis are flying for local models. But they are 10x less powerful in AI TOPS. And you can't upgrade the memory.
I really wish AMD and Intel boards get replaced by competent people. They could do it in very short time. Both have integrated GPUs with main memory. AMD and Intel have (or at least used to have) serious know-how in data buses and interconnects, respectively. But I don't see any of that happening.
ROCm? It can't even support decent Attention. It lacks a lot of features and NVIDIA is adding more each year. Soon they will reach escape velocity and nobody will catch them for a decade. smh
Granted, I feel like NVIDIA GPU pricing is such that Mac minis will be way less than 10x cheaper if not already, so one might still get ahead purchasing a bulk order of Mac minis....
> I really wish AMD and Intel boards get replaced by competent people.
Intel? Agreed. But AMD is making money hand over fist with enterprise AI stuff.
Right now, any effort that AMD or NVIDIA expend on the consumer sector is a waste of money that they could be spending making 10x more at the enterprise level on AI.
From all the existing examples, it really looks the most interesting.
I.e. what I'm surprised about is lack of backing for it from someone like AMD. It doesn't have to immediately replace ROCm, but AMD would benefit from it advancing and replacing the likes of CUDA.
I think Rust GPU is built on top of Vulkan + SPIR-V as their main foundation, not on top of CUDA or ROCm.
What I meant more is the language of writing GPU programs themselves, not necessarily the machinery right below it. Vulkan is good to advance for that.
I.e. CUDA and ROCm focus on C++ dialect as GPU language. Rust GPU does that with Rust and also relies on Vulkan without tying it to any specific GPU type.
If you don't want/need to program at lowest level possible, then Pytorch seems the obvious option for AMD support, or maybe Mojo. The Triton compiler would be another option for kernel writing.
to be honest, outside of fullstack and basic MCU stuff, these agents aren't very good. Whenever a sufficiently interesting new model comes out I test it on a couple problems for android app development and OS porting for novel cpu targets and we still haven't gotten there yet. I'd be happy to see a day where it was possible however
Just spent the last week or so porting TheRock to stagex in an effort to get ROCm built with a native musl/mimalloc toolchain and get it deterministic for high security/privacy workloads that cannot trust binaries only built with a single compiler.
It has been a bit of a nightmare and had to package like 30+ deps and their heavily customized LLVM, but got the runtime to build this morning finally.
Things are looking bright for high security workloads on AMD hardware due to them working fully in the open however much of a mess it may be.
I also attempted to package ROCM on musl. Specifically, packaging it for Alpine Linux.
It truly is a nightmare to build the whole thing. I got past the custom LLVM fork and a dozen other packages, but eventually decided it had been too much of a time sink.
I’m using llama.cpp with its vulkan support and it’s good enough for my uses. Vulkan so already there and just works. It’s probably on your host too, since so many other things rely on it anyway.
That said, I’d be curious to look at your build recipes. Maybe it can help power through the last bits of the Alpine port.
https://github.com/ROCm/TheRock/issues/3477 makes me quite sad for a variety of reasons. It shouldn't be like this. This work should be usable.
Oh I fully abandoned TheRock in my stagex ROCm build stack. It is not worth salvaging, but it was an incredibly useful reference for me to rewrite it.
C d Ty
AMD has years of catching up to do with ROCm just to get their devices to work well. They don't support all their own graphics cards that can do AI, and when it is supported, it's buggy. The AMDGPU graphics driver for Linux has had continued instability since 6.6. I don't understand why they can't hire better software engineers.
Because they aren't willing to pay for them?
ROCm is not supported on some very common consumer GPUs, e.g. the RX 580. Vulkan backends work just fine.
Same with my RX 5700. The supported ROCm version is too old to get Ollama running.
Did it used to be different?
A few years ago I thought I had used the ROCm drivers/libraries with hashcat on a RX580
Now it’s obsolete ?
RX 580 is a GCN 4 GPU. I'm pretty sure the bare minimum for ROCm is GCN 5 (Vega) and up.
In my experience fiddling with compute shaders a long time ago, cuda and rocm and opencv are way too much hassle to set up. Usually it takes a few hours to get the toolkits and SDK up and running that is, if you CAN get it up and running. The dependencies are way too big as well, cuda is 11gb??? Either way, just use Vulkan. Vulkan "just works" and doesn't lock you into Nvidia/amd.
Vulkan is a pain for different reasons. Easier to install sure, but you need a few hundred lines of code to set up shader compilation and resources, and you’ll need extensions to deal with GPU addresses like you can with CUDA.
Ah yes, but those hundred lines of code are basically free to produce now with LLMs...
> Challenger AMD’s ability to take data center GPU share from market leader Nvidia will certainly depend on the success or failure of its AI software stack, ROCm.
I don't think this is true. ROCm is a huge advantage for Nvidia but as far as I can tell it is more a set of R&D libraries than anything else, so all the Hot New Stuff keeps being Nvidia first and only (to start with) as the library ecosystem for the hotness doesn't exist yet. Then eventually new libraries are created that are CUDA independent and AMD turns out to make pretty good graphics cards.
I wouldn't be surprised of ROCm withered on the vine and AMD still does fine.
I've been using ROCm on my Radeon RX 6800 and my Ryzen AI 7 350 systems. I've only used it for GPU-accelerated rendering in Cycles, but I am glad that AMD has an option that isn't OpenCL now.
Someone from AMD posted this a few minutes ago, then deleted it:
"Anush's success is due to opting out of internal bureaucracy than anything else. most Claude use at AMD goes through internal infrastructure that can take hundreds of seconds per response due to throttling. Anush got us an exemption to use Anthropic directly. he is also exempt from normal policies on open source and so I can directly contribute to projects to add AMD support. He's an effective leader and has turned ROCm into a internal startup based in California. Definitely worth joining the team even if you've heard bad things about AMD as a whole."
This kind of bullshit is why I don't want to join AMD, even if this particular team is temporarily exempt from it.
> he is also exempt from normal policies on open source and so I can directly contribute to projects to add AMD support.
It's crazy that this is a big deal.
I understand the need for some kind of governance around this but for it to require a special exemption just shows how far the AMD culture needs to shift.
Liability is always a big deal.
So join NVIDIA instead
AMD hasn't signaled in behavior or words that they're going to actually support ROCm on $specificdevice for more than 4-5 years after release. Sometimes it's as little as the high 3.x years for shrinks like the consumer AMD RX 580. And often the ROCm support for consumer devices isn't out until a year after release, further cutting into that window.
Meanwhile nvidia just dropped CUDA/driver support for 1xxx series cards from their most recent drivers this year.
For me ROCm's mayfly lifetime is a dealbreaker.
Is it really that short? This support matrix shows ROCm 7.2.1 supporting quite old generations of GPUs, going back at least five or six years. I consider longevity important, too, but if they're actively supporting stuff released in 2020 (CDNA), I can't fault them too much. With open drivers on Linux, where all the real AI work is happening, I feel like this is a better longevity story than nvidia...where you're dependent on nvidia for kernel drivers in addition to CUDA.
https://rocm.docs.amd.com/en/latest/compatibility/compatibil...
Last year, AMD ran a GitHub poll for ROCm complaints and received more than 1,000 responses. Many were around supporting older hardware, which is today supported either by AMD or by the community, and one year on, all 1,000 complaints have been addressed, Elangovan said. AMD has a team going through GitHub complaints, but Elangovan continues to encourage developers to reach out on X where he’s always happy to listen.
Seems like they're making some effort in that direction at least. If you have specific concerns, maybe try hitting up Anush Elangovan on Twitter?
ROCm is open source and TheRock is community maintained, and in a minute the first Linux distro will have native in-tree builds. It will be supported for the foreseeable future due to AMDs open development approach.
It is Nvidia that has the track record of closed drivers and insisting on doing all software dev without community improvements to expected results.
> expected results
The defacto GPU compute platform? With the best featureset?
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I was thinking to get 2x r9700 for a home workstation (mostly inference). It is much cheaper than a similar nvidia build. But still not sure if good value or more trouble.
I own a single R9700 for the same reason you mentioned, looking into getting a second one. Was a lot of fiddling to get working on arch but RDNA4 and ROCm have come a long way. Every once in a while arch package updates break things but that’s not exclusive to ROCm.
LLM’s run great on it, it’s happily running gemma4 31b at the moment and I’m quite impressed. For the amount of VRAM you get it’s hard to beat, apart from the Intel cards maybe. But the driver support doesn’t seem to be that great there either.
Had some trouble with running comfyui, but it’s not my main use case, so I did not spent a lot of time figuring that out yet
1 reply →
Talking to friends who have fought more homelab battles than I ever will, my sense is that (1) AMD has done a better job with RDNA4 than the past generations, and (2) it seems very workload-dependent whether AMD consumer gear is "good value", "more trouble", or both at the same time.
Edit: I misread the "2x r9700" as "2 rx9700" which differs from the topic of this comment (about RNDA4 consumer SKUs). I'll keep my comment up, but anyone looking to get Radeon PRO cards can (should?) disregard.
1 reply →
I have this setup, with 2x 32Gb cards. It's perfect for my needs, and cheaper than anything comparable from NV.
Driver support eats directly into driver development
Apple got it right with unified memory with wide bus. That's why Mac Minis are flying for local models. But they are 10x less powerful in AI TOPS. And you can't upgrade the memory.
I really wish AMD and Intel boards get replaced by competent people. They could do it in very short time. Both have integrated GPUs with main memory. AMD and Intel have (or at least used to have) serious know-how in data buses and interconnects, respectively. But I don't see any of that happening.
ROCm? It can't even support decent Attention. It lacks a lot of features and NVIDIA is adding more each year. Soon they will reach escape velocity and nobody will catch them for a decade. smh
Granted, I feel like NVIDIA GPU pricing is such that Mac minis will be way less than 10x cheaper if not already, so one might still get ahead purchasing a bulk order of Mac minis....
A 5090 will cost you about the same amount of money as a Mac Studio M3 Ultra with eight times the RAM.
It's pretty insane how overpriced NVIDIA hardware is.
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> I really wish AMD and Intel boards get replaced by competent people.
Intel? Agreed. But AMD is making money hand over fist with enterprise AI stuff.
Right now, any effort that AMD or NVIDIA expend on the consumer sector is a waste of money that they could be spending making 10x more at the enterprise level on AI.
For many LLM load, it seems ROCm is slower than vulkan. What’s the point?
Side question, but why not advance something like Rust GPU instead as a general approach to GPU programming? https://github.com/Rust-GPU/rust-gpu/
From all the existing examples, it really looks the most interesting.
I.e. what I'm surprised about is lack of backing for it from someone like AMD. It doesn't have to immediately replace ROCm, but AMD would benefit from it advancing and replacing the likes of CUDA.
From the readme:
> Note: This project is still heavily in development and is at an early stage.
> Compiling and running simple shaders works, and a significant portion of the core library also compiles.
> However, many things aren't implemented yet. That means that while being technically usable, this project is not yet production-ready.
Also projects like rust gpu are built on top of projects like cuda and ROCm they aren’t alternatives they are abstractions overtop
I think Rust GPU is built on top of Vulkan + SPIR-V as their main foundation, not on top of CUDA or ROCm.
What I meant more is the language of writing GPU programs themselves, not necessarily the machinery right below it. Vulkan is good to advance for that.
I.e. CUDA and ROCm focus on C++ dialect as GPU language. Rust GPU does that with Rust and also relies on Vulkan without tying it to any specific GPU type.
If you don't want/need to program at lowest level possible, then Pytorch seems the obvious option for AMD support, or maybe Mojo. The Triton compiler would be another option for kernel writing.
I don't think that's something that can be pitched as a CUDA alternative. Just different level.
[dead]
Naive question, could agents help speed up building code for ROCm parity with CUDA? Outside of code, what are the bottlenecks for reaching parity?
Maybe this is dumb but at the moment through windows (and WSL?) you get: rocm DirectML Vulkan OpenML?
to be honest, outside of fullstack and basic MCU stuff, these agents aren't very good. Whenever a sufficiently interesting new model comes out I test it on a couple problems for android app development and OS porting for novel cpu targets and we still haven't gotten there yet. I'd be happy to see a day where it was possible however
I’ve found they’re quite good when you’re higher in the compiler stack, where it’s essentially a game of translating MLIR dialects.
Lack of focus from AMD management. See the sibling comment: https://news.ycombinator.com/item?id=47745611
They just don't care enough to compete.