It's unbearable that most LLM inference happens on closed H/W, closed drivers, and closed SDKs.
I would really appreciate your input!
1. Top end model on high/xhigh thinking (last time I did it it was Sol xhigh I think)
2. Make sure it creates some representative fixtures of different sizes and sets up a good testing, profiling and benchmarking loop that doesn't require my input.
3. Make sure it has access to reference implementation code
Edit: Oh and one obvious pitfall that for some reason I still have to remind even smart models of from time to time: make sure it knows not to try to parallelize its benchmark runs. I've occasionally had an agent struggle to figure out absolutely nonsensical data because it tried to run multiple tests on the same compute hardware simultaneously.
Sucks like important libraries like Alphafold are locked into CUDA. Its ridiculous for researchers.
My software is labeled "CPU only unless using an nVidia GPU". I would prefer to strikethrough "nVidia". Incidentally, this means no more Mac support.
Few problems I noticed:
* coopmat2 from nvidia is the classic embrace, extend, extinguish. No point to ask the models to translate from CUDA to coopmat2. Instead, the models can understand the existing CUDA and CPU kernels, and adapt them accordingly to non-nvidia devices.
* However, the standard API is also lacking. The models struggled to make prompt processing compute-bound on strix halo when the graph is complex. Upfront standard API might just be an evolution dead end.
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.
Intel uses SPIRV iirc
They're migrating away from SPIR-V to their own, Intel PISA: https://discourse.llvm.org/t/rfc-upstreaming-the-pisa-backen...
Vulkan's SPIR-V dialect is substantially different from the OpenCL one, notably with the former having structured control flow. They're incompatible between each other.
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.
Intel missed for a different reason - they didn't invest in the product space at all for decades. In terms of units they had the most market share (millions of Intel Integrated Graphics chips), but it wasn't seen as important. Bare minimum to render Windows and Office UI, nothing more.
Intel (via Codeplay) was handing it out on a silver platter - Nvidia on SYCL, full top chain, and people still wouldn’t want it.
By holistic, I mean hardware architecture cooperation. Nvidia can hold onto their lead forever if GPU designers fight over what a GPGPU hardware baseline looks like. The current ecosystem fragmentation is not competitive, and future fragmentation probably wouldn't work either. I think the fastest way to kill Nvidia would be a hardware consortium.
What would you expect the hardware consortium to coordinate on? Unified ISA?
Yes they have implementations on top of CUDA but they're maintained by... Intel. They didn't get buy-in for cross-vendor collaboration
Source: worked at a bunch of accelerator companies in the kernels or equivalent team. They're nothing alike.
My thinking was rather that most of our current programming languages put memory layout fully into the programmer’s responsibility - I can think off hand of a language where the compiler makes performance decisions like whether your structure are SoA, AoS or SoAoS, what alignment, padding, strides and float types to use.
Automatic decisions about when to use cooperative loads through shared local mem versus gathers from global mem and hardware caches are also something that such a hypothetical compiler would have to make.
As for ai and matrix vector performance... I mean matrices are extremely useful across many domains. The hands off language that exists today is called blas. That's fine but won't lead to the best perf on any GPU today.
SoA and AoS data layouts are not even a worthwhile point of contention. Same with shared v global mem. Today's hardware has specialized memory depending on the operation. The hardware on these processors is so specialized as to make anything but first class support for the feature be completely pointless. If you look at Nvidia code that's open source even they will special case a lot of their chips. Literally if you want the best perf you write the kernel exactly for the exact chip. That's intra vendor .. you can only imagine inter vendor
ZLUDA is already doing that, no?
the source language has never been the moat. Nvidia sells to hyperscalers. Hyperscalers have armies of kernel authors who have no issue translating shaders by hand (or now with claude). Nvidia's moat is (and will remain for the foreseeable future) the entire stack. you cannot fathom the pain and misery of working on literally any other stack. if you've never debugged a GPU synchronization error or kernel panic due to some GPU firmware bug or fought absolute shit profilers hunting for perf you really have no idea what you're talking about.
EDIT: i can't believe this really requires saying but graphics and compute are not the same domain at all. if you work in graphics for GPU but not compute then you are still way out of your depth commenting. to wit: graphics people do not (and cannot) write CUDA kernels/shaders.
You can simply disagree with me, regardless of my experience (or lack thereof).
that's exactly what i did and made an argument for why i think you're wrong. in response you provided exactly zero substantive remarks other than "i've written shaders" and then accused me of pissing.
also FYI it's clear from your profile that you've only worked on graphics (embree, blender, etc) and not compute. so i'll repeat: you're an outsider and you have absolutely no idea what you're talking about.
“if you've never debugged a GPU synchronization error or kernel panic due to some GPU firmware bug or fought absolute shit profilers hunting for perf”
I have done all of those things. As part of my full time job, for years.
Now that we’ve put all of that aside, can we stop talking about me and go back to discussing moats? What do you think are top three things that are holding customers back from buying AMD GPUs instead of Nvidia GPUs?
I could be misremembering, but I think Jensen Huang himself once called CUDA or the CUDA ecosystem their moat, and it certainly seems to be accepted narrative in the tech press. They may be wrong there, and you sharing your first hand experience here would be helpful to many of us readers here.
My 7900XTX is supported, I run local models via rocm all the time recently, mostly to play with/experiment on, Vulcan works as well and for some models works better (or the trade offs are better for that use case).
Their mistake was simply not picking and going all in earlier, they let nvidia become the defacto standard without even contesting it on both the hardware side and software side and that’s a hard though not impossible comeback to make.
Long term I think they’ll catch up in capability if not market share because simply too much money on the table not to.
https://rocm.docs.amd.com/_/downloads/radeon-ryzen/en/docs-6...
“graphics people do not (and cannot) write CUDA kernels/shaders” is just not true at all. All it would take to verify that would be things like reading the introduction of the OptiX documentation, a small sample of SIGGRAPH GPU papers or the Blender/Cycles source code.
AMD GPUs build for AI specs for reference: https://flopper.io/gpus?vendor=AMD&page=1
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
But to reason in other direction, unless you absolutely need cards right now, you could throw that ~$1440 in a 6 month CD and let the 4.5% pay for the tax or shipping on a 10090 XT or whatever RDNA 5 flagship when those drop in about as much time. If it lands anywhere close to what the rumors are indicating it should be a fucking monster.
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.