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And yet they still can't solve the problem of their GPU driver/software stack for ML being much worse than NVidia's. It seems like the first step is easy: pay m

And yet they still can't solve the problem of their GPU driver/software stack for ML being much worse than NVidia's. It seems like the first step is easy: pay more for engineers. AMD pays engineers significantly less than NVidia, and it's presumably quite hard to build a competitive software stack while paying so much less. You get what you pay for.

partypete 2y ago
Came here to say this. They only just recently got an AMD GPU on MLPerf thanks to a (different company), Tinycorp by George Hotz. I guess basic ML performance is too hard a problem.
- bee_rider 2y ago
  I dunno, it a world where hardware companies like, sold hardware, and then software companies wrote software and sold that could be pretty nice. It is cool that Hotz is doing something other than contribute to an anticompetitive company’s moat.
wordofx 2y ago
And yet people seem to work just fine with ML on AMD GPUs when they aren’t thinking about Jensen.
- logicchains 2y ago
  Which AMD GPUs? Most consumer AMD GPUs don't even support ROCm.
  - lhl 2y ago
    ROCm 6.0 and 6.1 list RDNA3 (gfx1100) and RDNA2 (gfx1030) in their supported architectures list: https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html https://rocm.docs.amd.com/en/latest/compatibility/compatibil... Although "official" / validated support^ is only for PRO W6800/V620 for RDNA2 and RDNA3 RX 7900's for consumer. Based on lots of reports you can probably just HSA_OVERRIDE_GFX_VERSION override for other RDNA2/3 cards and it'll probably just work. I can get GPU-accelerate ROCm for LLM inferencing on my Radeon 780M iGPU for example w/ ROCm 6.0 and HSA_OVERRIDE_GFX_VERSION=11.0.0 (In the past some people also built custom versions of ROCm for older architectures (eg ROC_ENABLE_PRE_VEGA=1) but I have no idea if those work still or not.) ^ https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html https://rocm.docs.amd.com/projects/install-on-linux/en/lates...
  - JonChesterfield 2y ago
    Debian, Arch and Gentoo have ROCm built for consumer GPUs. Thus so do their derivatives. Anything gfx9 or later is likely to be fine and gfx8 has a decent chance of working. The https://github.com/ROCm/ROCm https://github.com/ROCm/ROCm source has build scripts these days. At least some of the internal developers largely work on consumer hardware. It's not as solid as the enterprise gear but it's also very cheap so overall that seems reasonable to me. I'm using a pair of 6900XT, with a pair of VII's in a backup machine. For turn key proprietary stuff where you really like the happy path foreseen by your vendor, in classic mainframe style, team green is who you want.
    - paulmd 2y ago
      > For turn key proprietary stuff where you really like the happy path foreseen by your vendor there really was no way for AMD to foresee that people might want to run GPGPU workloads on their polaris cards? isn't that a little counterfactual to the whole OpenCL and HSA Framework push predating that? Example: it's not that things like Bolt didn't exist to try and compete with Thrust... it's that the NVIDIA one has had three updates in the last month and Bolt was last updated 10 years ago. You're literally reframing "having working runtime and framework support for your hardware" as being some proprietary turnkey luxury for users, as well as an unforeseeable eventuality for AMD. It wasn't a development priority, but users do like to actually build code that works etc. That's why you got kicked to the curb by Blender - your OpenCL wasn't stable even after years of work from them and you. That's why you got kicked to the curb by Octane - your Vulkan Compute support wasn't stable enough to even compile their code successfully. That's the story that's related by richg42 about your OpenGL driver implementation too - that it's just paper features and resume-driven development by developers 10 years departed all the way down. The issues discussed by geohotz aren't new, and they aren't limited to ROCm or deep learning in general. This is, broadly speaking, the same level of quality that AMD has applied to all its software for decades. And the social-media "red team" loyalism strategy doesn't really work here, you can't push this into "AMD drivers have been good for like 10 years now!!!" fervor when the understanding of the problems are that broad and that collectively shared and understood. Every GPGPU developer who's tried has bounced off this AMD experience for literally an entire generation running now. The shared collective experience is that AMD is not serious in the field, and it's difficult to believe it's a good-faith change and interest in advancing the field rather than just a cashgrab. It's also completely foreseeable that users want broad, official support for all their architectures, and not one or two specifics etc. Like these aren't mysteries that AMD just accidentally forgot about, etc. They're basic asks that you are framing as "turnkey proprietary stuff", like a working opencl runtime or a working spir-v compiler. What was it linus said about the experience of working with NVIDIA? That's been the experience of the GPGPU community working with AMD, for decades. Shit is broken and doesn't work, and there's no interest in making it otherwise. And the only thing that changed it is a cashgrab, and a working compiler/runtime is still "turnkey proprietary stuff" they have to be arm-twisted into doing by Literally Being Put On Blast By Geohotz Until It's Fixed. "Fuck you, AMD" is a sentiment that there is very valid reasons to feel given the amount of needless suffering you have generated - but we just don't do that to red team, do we? But you guys have been more intransigent about just supporting GPGPU, no matter what framework, please just pick one, get serious and start working already than NVIDIA ever was about wayland etc. You've blown decades just refusing to ever shit or get off the pot (without even giving enough documentation for the community to just do it themselves). And that's not an exaggeration - I bounced off the AMD stack in 2012, and it wasn't a new problem then either. It's too late for "we didn't know people wanted a working runtime or to develop on gaming cards" to work as an excuse, after decades of overt willing neglect it's just patronizing. Again, sorry, this is ranty, it's not that I'm upset at you personally etc, but like, my advice as a corporate posture here is don't go looking for a ticker-tape parade for finally delivering a working runtime that you've literally been advertising support for for more than a decade like it's some favor to the community. These aren't "proprietary turnkey features" they're literally the basics of the specs you're advertising compliance with, and it's not even just one it's like 4+ different APIs that have this problem with you guys that has been widely known, discussed in tech blogs etc for more than a decade (richg42). I've been saying it for a long time, so has everyone else who's ever interacted with AMD hardware in the GPGPU space. Nobody there cared until it was a cashgrab, actually half the time you get the AMD fan there to tell you the drivers have been good for a decade now (AMD cannot fail, only be failed). It's frustrating. You've poisoned the well with generations of developers, with decades of corporate obstinance that would make NVIDIA blush, please at least have a little contrition about the whole experience and the feelings on the other side here.
- FeepingCreature 2y ago
  I have a 7900 XTX. There's a known firmware crash issue with ComfyUI. It's been reported like a year ago. Every rocm patch release I check the notes, and every release it goes unfixed. That's not to go into the intense jank that is the rocm debian repo. If we need DL at work, I'll recommend Nvidia, no question.
sitkack 2y ago
Everyone does software poorly, hardware companies more so.
- jajko 2y ago
  Well this is glaringly obvious to whole world, and Nvidia managed to get it right. Surely a feat that can be repeated elsewhere when enough will is spread over some time. And it would make them grow massively, something no shareholder ever frowns upon.
  - rf15 2y ago
    > Nvidia managed to get it right I don't think they did. If you work in the space and watched it develop over the years, you can see that there's been (and still are) plenty of jank and pain points to be found. Their spread mostly comes from their dominant market position before, afaik.
    - modeless 2y ago
      They succeeded not because they are perfect but because they are the least bad. By far.
      - DanielHB 2y ago
        Also CUDA first released in 2007, I remember researching GPUs at the time and wondering what the hell CUDA was (I was a teenager). They were VERY early to the party and has A LOT of time to improve. Everyone is catching up to ~10 years of a headstart.
      - croes 2y ago
        Or people are just used to the same kind of bad
cepth 2y ago
A couple of thoughts here. * AMD's traditional target market for its GPUs has been HPC as opposed to deep learning/"AI" customers. For example, look at the supercomputers at the national labs. AMD has won quite a few high profile bids with the national labs in recent years: - Frontier (deployment begun in 2021) (https://en.wikipedia.org/wiki/Frontier_(supercomputer) https://en.wikipedia.org/wiki/Frontier_(supercomputer)) - used at Oak Ridge for modeling nuclear reactors, materials science, biology, etc. - El Capitan (2023) (https://en.wikipedia.org/wiki/El_Capitan_(supercomputer) https://en.wikipedia.org/wiki/El_Capitan_(supercomputer)) - Livermore national lab AMD GPUs are pretty well represented on the TOP500 list (https://top500.org/lists/top500/list/2024/06/ https://top500.org/lists/top500/list/2024/06/), which tends to feature computers used by major national-level labs for scientific research. AMD CPUs are even moreso represented. * HPC tends to focus exclusively on FP64 computation, since rounding errors in that kind of use-case are a much bigger deal than in DL (see for example https://hal.science/hal-02486753/document https://hal.science/hal-02486753/document). NVIDIA innovations like TensorFloat, mixed precision, custom silicon (e.g., the "transformer engine") are of limited interest to HPC customers. It's no surprise that AMD didn't pursue similar R&D, given who they were selling GPUs to. * People tend to forget that less than a decade ago, AMD as a company had a few quarters of cash left before the company would've been bankrupt. When Lisa Su took over as CEO in 2014, AMD market share for all CPUs was 23.4% (even lower in the more lucrative datacenter market). This would bottom out at 17.8% in 2016 (https://www.trefis.com/data/companies/AMD,.INTC/no-login-required/TfU13vlN/Intel-vs-AMD-How-Have-Revenues-Key-Operating-Metrics-Changed-Over-Recent-Years https://www.trefis.com/data/companies/AMD,.INTC/no-login-req...). AMD's "Zen moment" didn't arrive until March 2017. And it wasn't until Zen 2 (July 2019), that major datacenter customers began to adopt AMD CPUs again. * In interviews with key AMD figures like Mark Papermaster and Forrest Norrod, they've mentioned how in the years leading up to the Zen release, all other R&D was slashed to the bone. You can see (https://www.statista.com/statistics/267873/amds-expenditure-on-research-and-development-since-2001 https://www.statista.com/statistics/267873/amds-expenditure-...) that AMD R&D spending didn't surpass its previous peak (on a nominal dollar, not even inflation-adjusted, basis) until 2020. There was barely enough money to fund the CPUs that would stop the company from going bankrupt, much less fund GPU hardware and software development. * By the time AMD could afford to spend on GPU development, CUDA was the entrenched leader. CUDA was first released in 2003(!), ROCm not until 2016. AMD is playing from behind, and had to make various concessions. The ROCm API is designed around CUDA API verbs/nouns. AMD funded ZLUDA, intended to be a "translation layer" so that CUDA programs can run as a drop-in on ROCm. * There's a chicken-and-egg problem here. 1) There's only one major cloud (Azure) that has ready access to AMD's datacenter-grade GPUs (the Instinct series). 2) I suspect a substantial portion of their datacenter revenue still comes from traditional HPC customers, who have no need for the ROCm stack. 3) The lack of a ROCm developer ecosystem means that development and bug fixes come much slower than they would for CUDA. For example, the mainline TensorFlow release was broken on ROCm for a while (you had to install the nightly release). 4) But, things are improving (slowly). ROCm 6 works substantially better than ROCm 5 did for me. PyTorch and TensorFlow benchmark suites will run. Trust me, I share the frustration around the semi-broken state that ROCm is in for deep learning applications. As an owner of various NVIDIA GPUs (from consumer laptop/desktop cards to datacenter accelerators), in 90% of cases things just work on CUDA. On ROCm, as of today it definitely doesn't "just work". I put together a guide for Framework laptop owners to get ROCm working on the AMD GPU that ships as an optional add-in (https://community.frame.work/t/installing-rocm-hiplib-on-ubuntu-22-04/50412/2 https://community.frame.work/t/installing-rocm-hiplib-on-ubu...). This took a lot of head banging, and the parsing of obscure blogs and Github issues. TL;DR, if you consider where AMD GPUs were just a few years ago, things are much better now. But, it still takes too much effort for the average developer to get started on ROCm today.
Bancakes 2y ago
Exactly. ROCm is only available for the top tier RX 7900 GPUs and you’re expected to run Linux. AMD was (is) tinkering with a translation layer for CUDA, much like how WINE translates directX. Great idea but it’s been taking a while in this fast paced market.
- drbscl 2y ago
  > AMD was (is) tinkering with a translation layer for CUDA From what I understand, they dropped the contract with the engineer who was working on it. Fortunately, as part of the contract, said engineer stipulated that the project would become open source, so now it is, and is still being maintained by that engineer.
- anticensor 2y ago
  > ROCm is only available for the top tier RX 7900 GPUs and you’re expected to run Linux. Fixed it for you: ROCm is only officially supported for the top tier RX 7900 GPUs and you’re expected to run Linux. Desktop class cards work if you apply a "HSA version override".
  - Bancakes 2y ago
    Cool. I was thinking of getting a 7800 XT over a 4070. Hope I can get llama 70B working nearly as well.

partypete 2y ago

Came here to say this. They only just recently got an AMD GPU on MLPerf thanks to a (different company), Tinycorp by George Hotz. I guess basic ML performance is too hard a problem.

bee_rider 2y ago
I dunno, it a world where hardware companies like, sold hardware, and then software companies wrote software and sold that could be pretty nice. It is cool that Hotz is doing something other than contribute to an anticompetitive company’s moat.

wordofx 2y ago

And yet people seem to work just fine with ML on AMD GPUs when they aren’t thinking about Jensen.

logicchains 2y ago
Which AMD GPUs? Most consumer AMD GPUs don't even support ROCm.
- lhl 2y ago
  ROCm 6.0 and 6.1 list RDNA3 (gfx1100) and RDNA2 (gfx1030) in their supported architectures list: https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html https://rocm.docs.amd.com/en/latest/compatibility/compatibil... Although "official" / validated support^ is only for PRO W6800/V620 for RDNA2 and RDNA3 RX 7900's for consumer. Based on lots of reports you can probably just HSA_OVERRIDE_GFX_VERSION override for other RDNA2/3 cards and it'll probably just work. I can get GPU-accelerate ROCm for LLM inferencing on my Radeon 780M iGPU for example w/ ROCm 6.0 and HSA_OVERRIDE_GFX_VERSION=11.0.0 (In the past some people also built custom versions of ROCm for older architectures (eg ROC_ENABLE_PRE_VEGA=1) but I have no idea if those work still or not.) ^ https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html https://rocm.docs.amd.com/projects/install-on-linux/en/lates...
- JonChesterfield 2y ago
  Debian, Arch and Gentoo have ROCm built for consumer GPUs. Thus so do their derivatives. Anything gfx9 or later is likely to be fine and gfx8 has a decent chance of working. The https://github.com/ROCm/ROCm https://github.com/ROCm/ROCm source has build scripts these days. At least some of the internal developers largely work on consumer hardware. It's not as solid as the enterprise gear but it's also very cheap so overall that seems reasonable to me. I'm using a pair of 6900XT, with a pair of VII's in a backup machine. For turn key proprietary stuff where you really like the happy path foreseen by your vendor, in classic mainframe style, team green is who you want.
  - paulmd 2y ago
    > For turn key proprietary stuff where you really like the happy path foreseen by your vendor there really was no way for AMD to foresee that people might want to run GPGPU workloads on their polaris cards? isn't that a little counterfactual to the whole OpenCL and HSA Framework push predating that? Example: it's not that things like Bolt didn't exist to try and compete with Thrust... it's that the NVIDIA one has had three updates in the last month and Bolt was last updated 10 years ago. You're literally reframing "having working runtime and framework support for your hardware" as being some proprietary turnkey luxury for users, as well as an unforeseeable eventuality for AMD. It wasn't a development priority, but users do like to actually build code that works etc. That's why you got kicked to the curb by Blender - your OpenCL wasn't stable even after years of work from them and you. That's why you got kicked to the curb by Octane - your Vulkan Compute support wasn't stable enough to even compile their code successfully. That's the story that's related by richg42 about your OpenGL driver implementation too - that it's just paper features and resume-driven development by developers 10 years departed all the way down. The issues discussed by geohotz aren't new, and they aren't limited to ROCm or deep learning in general. This is, broadly speaking, the same level of quality that AMD has applied to all its software for decades. And the social-media "red team" loyalism strategy doesn't really work here, you can't push this into "AMD drivers have been good for like 10 years now!!!" fervor when the understanding of the problems are that broad and that collectively shared and understood. Every GPGPU developer who's tried has bounced off this AMD experience for literally an entire generation running now. The shared collective experience is that AMD is not serious in the field, and it's difficult to believe it's a good-faith change and interest in advancing the field rather than just a cashgrab. It's also completely foreseeable that users want broad, official support for all their architectures, and not one or two specifics etc. Like these aren't mysteries that AMD just accidentally forgot about, etc. They're basic asks that you are framing as "turnkey proprietary stuff", like a working opencl runtime or a working spir-v compiler. What was it linus said about the experience of working with NVIDIA? That's been the experience of the GPGPU community working with AMD, for decades. Shit is broken and doesn't work, and there's no interest in making it otherwise. And the only thing that changed it is a cashgrab, and a working compiler/runtime is still "turnkey proprietary stuff" they have to be arm-twisted into doing by Literally Being Put On Blast By Geohotz Until It's Fixed. "Fuck you, AMD" is a sentiment that there is very valid reasons to feel given the amount of needless suffering you have generated - but we just don't do that to red team, do we? But you guys have been more intransigent about just supporting GPGPU, no matter what framework, please just pick one, get serious and start working already than NVIDIA ever was about wayland etc. You've blown decades just refusing to ever shit or get off the pot (without even giving enough documentation for the community to just do it themselves). And that's not an exaggeration - I bounced off the AMD stack in 2012, and it wasn't a new problem then either. It's too late for "we didn't know people wanted a working runtime or to develop on gaming cards" to work as an excuse, after decades of overt willing neglect it's just patronizing. Again, sorry, this is ranty, it's not that I'm upset at you personally etc, but like, my advice as a corporate posture here is don't go looking for a ticker-tape parade for finally delivering a working runtime that you've literally been advertising support for for more than a decade like it's some favor to the community. These aren't "proprietary turnkey features" they're literally the basics of the specs you're advertising compliance with, and it's not even just one it's like 4+ different APIs that have this problem with you guys that has been widely known, discussed in tech blogs etc for more than a decade (richg42). I've been saying it for a long time, so has everyone else who's ever interacted with AMD hardware in the GPGPU space. Nobody there cared until it was a cashgrab, actually half the time you get the AMD fan there to tell you the drivers have been good for a decade now (AMD cannot fail, only be failed). It's frustrating. You've poisoned the well with generations of developers, with decades of corporate obstinance that would make NVIDIA blush, please at least have a little contrition about the whole experience and the feelings on the other side here.
FeepingCreature 2y ago
I have a 7900 XTX. There's a known firmware crash issue with ComfyUI. It's been reported like a year ago. Every rocm patch release I check the notes, and every release it goes unfixed. That's not to go into the intense jank that is the rocm debian repo. If we need DL at work, I'll recommend Nvidia, no question.

sitkack 2y ago

Everyone does software poorly, hardware companies more so.

jajko 2y ago
Well this is glaringly obvious to whole world, and Nvidia managed to get it right. Surely a feat that can be repeated elsewhere when enough will is spread over some time. And it would make them grow massively, something no shareholder ever frowns upon.
- rf15 2y ago
  > Nvidia managed to get it right I don't think they did. If you work in the space and watched it develop over the years, you can see that there's been (and still are) plenty of jank and pain points to be found. Their spread mostly comes from their dominant market position before, afaik.
  - modeless 2y ago
    They succeeded not because they are perfect but because they are the least bad. By far.
    - DanielHB 2y ago
      Also CUDA first released in 2007, I remember researching GPUs at the time and wondering what the hell CUDA was (I was a teenager). They were VERY early to the party and has A LOT of time to improve. Everyone is catching up to ~10 years of a headstart.
    - croes 2y ago
      Or people are just used to the same kind of bad

cepth 2y ago

A couple of thoughts here. * AMD's traditional target market for its GPUs has been HPC as opposed to deep learning/"AI" customers. For example, look at the supercomputers at the national labs. AMD has won quite a few high profile bids with the national labs in recent years: - Frontier (deployment begun in 2021) (https://en.wikipedia.org/wiki/Frontier_(supercomputer) https://en.wikipedia.org/wiki/Frontier_(supercomputer)) - used at Oak Ridge for modeling nuclear reactors, materials science, biology, etc. - El Capitan (2023) (https://en.wikipedia.org/wiki/El_Capitan_(supercomputer) https://en.wikipedia.org/wiki/El_Capitan_(supercomputer)) - Livermore national lab AMD GPUs are pretty well represented on the TOP500 list (https://top500.org/lists/top500/list/2024/06/ https://top500.org/lists/top500/list/2024/06/), which tends to feature computers used by major national-level labs for scientific research. AMD CPUs are even moreso represented. * HPC tends to focus exclusively on FP64 computation, since rounding errors in that kind of use-case are a much bigger deal than in DL (see for example https://hal.science/hal-02486753/document https://hal.science/hal-02486753/document). NVIDIA innovations like TensorFloat, mixed precision, custom silicon (e.g., the "transformer engine") are of limited interest to HPC customers. It's no surprise that AMD didn't pursue similar R&D, given who they were selling GPUs to. * People tend to forget that less than a decade ago, AMD as a company had a few quarters of cash left before the company would've been bankrupt. When Lisa Su took over as CEO in 2014, AMD market share for all CPUs was 23.4% (even lower in the more lucrative datacenter market). This would bottom out at 17.8% in 2016 (https://www.trefis.com/data/companies/AMD,.INTC/no-login-required/TfU13vlN/Intel-vs-AMD-How-Have-Revenues-Key-Operating-Metrics-Changed-Over-Recent-Years https://www.trefis.com/data/companies/AMD,.INTC/no-login-req...). AMD's "Zen moment" didn't arrive until March 2017. And it wasn't until Zen 2 (July 2019), that major datacenter customers began to adopt AMD CPUs again. * In interviews with key AMD figures like Mark Papermaster and Forrest Norrod, they've mentioned how in the years leading up to the Zen release, all other R&D was slashed to the bone. You can see (https://www.statista.com/statistics/267873/amds-expenditure-on-research-and-development-since-2001 https://www.statista.com/statistics/267873/amds-expenditure-...) that AMD R&D spending didn't surpass its previous peak (on a nominal dollar, not even inflation-adjusted, basis) until 2020. There was barely enough money to fund the CPUs that would stop the company from going bankrupt, much less fund GPU hardware and software development. * By the time AMD could afford to spend on GPU development, CUDA was the entrenched leader. CUDA was first released in 2003(!), ROCm not until 2016. AMD is playing from behind, and had to make various concessions. The ROCm API is designed around CUDA API verbs/nouns. AMD funded ZLUDA, intended to be a "translation layer" so that CUDA programs can run as a drop-in on ROCm. * There's a chicken-and-egg problem here. 1) There's only one major cloud (Azure) that has ready access to AMD's datacenter-grade GPUs (the Instinct series). 2) I suspect a substantial portion of their datacenter revenue still comes from traditional HPC customers, who have no need for the ROCm stack. 3) The lack of a ROCm developer ecosystem means that development and bug fixes come much slower than they would for CUDA. For example, the mainline TensorFlow release was broken on ROCm for a while (you had to install the nightly release). 4) But, things are improving (slowly). ROCm 6 works substantially better than ROCm 5 did for me. PyTorch and TensorFlow benchmark suites will run. Trust me, I share the frustration around the semi-broken state that ROCm is in for deep learning applications. As an owner of various NVIDIA GPUs (from consumer laptop/desktop cards to datacenter accelerators), in 90% of cases things just work on CUDA. On ROCm, as of today it definitely doesn't "just work". I put together a guide for Framework laptop owners to get ROCm working on the AMD GPU that ships as an optional add-in (https://community.frame.work/t/installing-rocm-hiplib-on-ubuntu-22-04/50412/2 https://community.frame.work/t/installing-rocm-hiplib-on-ubu...). This took a lot of head banging, and the parsing of obscure blogs and Github issues. TL;DR, if you consider where AMD GPUs were just a few years ago, things are much better now. But, it still takes too much effort for the average developer to get started on ROCm today.

Bancakes 2y ago

Exactly. ROCm is only available for the top tier RX 7900 GPUs and you’re expected to run Linux. AMD was (is) tinkering with a translation layer for CUDA, much like how WINE translates directX. Great idea but it’s been taking a while in this fast paced market.

drbscl 2y ago
> AMD was (is) tinkering with a translation layer for CUDA From what I understand, they dropped the contract with the engineer who was working on it. Fortunately, as part of the contract, said engineer stipulated that the project would become open source, so now it is, and is still being maintained by that engineer.
anticensor 2y ago
> ROCm is only available for the top tier RX 7900 GPUs and you’re expected to run Linux. Fixed it for you: ROCm is only officially supported for the top tier RX 7900 GPUs and you’re expected to run Linux. Desktop class cards work if you apply a "HSA version override".
- Bancakes 2y ago
  Cool. I was thinking of getting a 7800 XT over a 4070. Hope I can get llama 70B working nearly as well.