AI will take down Nvidia’s moat. When it becomes trivial to translate CUDA/PTX to HIP, SYCL or Metal, CUDA is no longer the moat, it becomes the intermediate representation.
On the SDK front, you tried just having an agent reimplement the model you're interested in and just use the weights? I've taken to treating off the shelf implementations as reference implementations anyway, because I can often squeeze out significantly better performance for my configuration and use case by having Codex hammer at it for a few hours.
I would too, but sadly that's Khronos' job to organize, and they've had trouble getting American vendors to work together.
It's likely that CUDA will continue dominating until they put aside their differences. The current MLX/MPS/ROCm ecosystems are too fractured to threaten Nvidia.
I'm talking about holistic efforts like OpenCL, and standards that would be equivalent to Nvidia's "Compute Capability" versioning.
The basic underlying tech can be agreed on, but Apple/AMD/Intel all have different GPU priorities that limit their ability to agree on a CUDA-adjacent hardware platform.
What do you mean by holistic? SYCL is an open versioned standard that allows for vendor specific extensions. The problem is not that there isn’t a proper standard, the problem is that many hardware vendors - or software developers simply don’t want to adopt it.
Intel (via Codeplay) was handing it out on a silver platter - Nvidia on SYCL, full top chain, and people still wouldn’t want it.
RDNA1 isn't good for a whole lot, even flagship RDNA2 cards are a stretch for many things. The lack of WMMA/matrix multiply/BF16 is too severe of a penalty.
The FP16 throughput on RDNA1 is both shader reliant and requires everything to be packed first. Even with 2 or 4 or 1000 cards, you would be consuming all of the available memory and memory bandwidth just packing and unpacking values, and if you really want to dump a hundred billion tokens into making it work anyways, you're only going to find out that even if you bother to sit there ferrying packed values to ram or disk before then issuing the instructions, paying that already severe penalty again when the values then have to be unpacked is so steep of a cost that the 256 BF16 flops/cu/clock's effective throughput is outright lower than simply doing it on a Zen 2 processor. You also don't have INT8 (or really INT4) on RDNA1 so the other RNS/CRT tricks aren't viable.
Sadly RDNA1's VCN2 also lacks actually good x264 bframe encoding support, or even P010 for 10 bit color, so what I'm saying is you should sell them. Used Radeon VII's are like $260, you'll go a lot further with those especially if you throw in a 7900XTX, and then augment that further with a 9070 CRE (you only want it for its int8 cores), and of course 128GB of ram.
E: And sure, that's 3, or ideally 4 GPUs, and a good bit of extra work. But that gets you up to more than halfway to the naive performance of a $15,000 MI300x in a surprising amount of cases, with additional strengths that it lacks. For far less than half of the cost
So far, it has only been tested on my RX 9060 XT (gfx1200), where I’ve used it with CUDA-enabled LibTorch workloads, including long ai training and use.
I also added a GPU scanner / auto-detection system that detects:
AMD GPU model
gfxXXXX architecture
ROCm/HIP installation
driver info
whether the GPU has already been validated by the project
The goal now is to test it on more hardware, especially RX 6000 / 7000 / 9000 cards.
If you have an AMD GPU on Windows and want to try it, I’d really appreciate compatibility reports working or broken. There’s a dedicated GPU compatibility issue template in the repo.
If this is useful to you, a star would also help the project get more testers.
AI will take down Nvidia’s moat. When it becomes trivial to translate CUDA/PTX to HIP, SYCL or Metal, CUDA is no longer the moat, it becomes the intermediate representation.
Off-topic and somewhat of a rant, but I'd far prefer us all focusing on open standards like HIP, SYCL, OpenCL, etc.
It's unbearable that most LLM inference happens on closed H/W, closed drivers, and closed SDKs.
On the SDK front, you tried just having an agent reimplement the model you're interested in and just use the weights? I've taken to treating off the shelf implementations as reference implementations anyway, because I can often squeeze out significantly better performance for my configuration and use case by having Codex hammer at it for a few hours.
Could you share the prompts and workflow? I’ve tried this too, but with mixed success when it comes to creating custom tile kernels.
I would really appreciate your input!
I would too, but sadly that's Khronos' job to organize, and they've had trouble getting American vendors to work together.
It's likely that CUDA will continue dominating until they put aside their differences. The current MLX/MPS/ROCm ecosystems are too fractured to threaten Nvidia.
Maybe a not-Khronos org should try
Wdym American Vendors?
Intel uses SPIRV iirc
I'm talking about holistic efforts like OpenCL, and standards that would be equivalent to Nvidia's "Compute Capability" versioning.
The basic underlying tech can be agreed on, but Apple/AMD/Intel all have different GPU priorities that limit their ability to agree on a CUDA-adjacent hardware platform.
What do you mean by holistic? SYCL is an open versioned standard that allows for vendor specific extensions. The problem is not that there isn’t a proper standard, the problem is that many hardware vendors - or software developers simply don’t want to adopt it.
Intel (via Codeplay) was handing it out on a silver platter - Nvidia on SYCL, full top chain, and people still wouldn’t want it.
I made cuda-metal btw (for mac kek), https://github.com/lulzx/cuda-metal
I wish there were a way to use RDNA1 cards with CUDA for AMD. My 5700XTs are sitting in a drawer.
RDNA1 isn't good for a whole lot, even flagship RDNA2 cards are a stretch for many things. The lack of WMMA/matrix multiply/BF16 is too severe of a penalty.
The FP16 throughput on RDNA1 is both shader reliant and requires everything to be packed first. Even with 2 or 4 or 1000 cards, you would be consuming all of the available memory and memory bandwidth just packing and unpacking values, and if you really want to dump a hundred billion tokens into making it work anyways, you're only going to find out that even if you bother to sit there ferrying packed values to ram or disk before then issuing the instructions, paying that already severe penalty again when the values then have to be unpacked is so steep of a cost that the 256 BF16 flops/cu/clock's effective throughput is outright lower than simply doing it on a Zen 2 processor. You also don't have INT8 (or really INT4) on RDNA1 so the other RNS/CRT tricks aren't viable.
Sadly RDNA1's VCN2 also lacks actually good x264 bframe encoding support, or even P010 for 10 bit color, so what I'm saying is you should sell them. Used Radeon VII's are like $260, you'll go a lot further with those especially if you throw in a 7900XTX, and then augment that further with a 9070 CRE (you only want it for its int8 cores), and of course 128GB of ram.
E: And sure, that's 3, or ideally 4 GPUs, and a good bit of extra work. But that gets you up to more than halfway to the naive performance of a $15,000 MI300x in a surprising amount of cases, with additional strengths that it lacks. For far less than half of the cost
I agree, I should've sold them last year when they hit $500 each. I have like 8 of them from my mining days. What a silly mistake I made.
CUDA for AMD on Windows
I’ve been working on a Windows setup that lets CUDA-targeted applications run on AMD GPUs using ZLUDA + ROCm/HIP.
Repo: https://github.com/Speedstu/CUDA-for-AMD-Windows
So far, it has only been tested on my RX 9060 XT (gfx1200), where I’ve used it with CUDA-enabled LibTorch workloads, including long ai training and use.
I also added a GPU scanner / auto-detection system that detects:
AMD GPU model
gfxXXXX architecture
ROCm/HIP installation
driver info
whether the GPU has already been validated by the project
Example:
RX 9060 XT → gfx1200 → RDNA4 → HIP detected → validated
The goal now is to test it on more hardware, especially RX 6000 / 7000 / 9000 cards.
If you have an AMD GPU on Windows and want to try it, I’d really appreciate compatibility reports working or broken. There’s a dedicated GPU compatibility issue template in the repo.
If this is useful to you, a star would also help the project get more testers.
(As a side note, I love the name ZLUDA; it very aptly means "delusion" or "deception" in Polish.)