TIL, I didn’t know that. I always assumed it came from level zero”, the Intel computer layer that ZLUDA was translating to before its developer was hired by AMD to target HIP.
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.
It's volatile as all get out and I'm not going to call out any token by name but if you needed another nudge, you can absolutely cover the cost of power and the 9070 GRE (or even XT) itself before the warranty expires by mining (without holding/speculating), which has been my breakeven point for sidestepping any guilt I might feel from buying another flagship GPU.
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 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.
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.
The problem with the open standards is that their dev UX is absolutely horrible. You can't neglect usability, and then be surprised that there are no users.
This is at the core of the matter for me, and my knowledge is too weak to understand why this is the case. I don't enjoy the idea of relying on Nvidia's stack for GPU compute, but the alternatives I've tried (e.g. Vulkan compute) are higher friction to use. I am trying to reconcile why; Nvidia shouldn't have this moat.
My software is labeled "CPU only unless using an nVidia GPU". I would prefer to strikethrough "nVidia". Incidentally, this means no more Mac support.
It's impossible to have an open standard. Hardware accelerators are nothing alike and have different perf characteristics. Each kernel is tuned to the hardware. The idea of writing a performant kernel in opencl is a fantasy.
Source: worked at a bunch of accelerator companies in the kernels or equivalent team. They're nothing alike.
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.
I don't think we're at a point yet where anyone would trust ZLUDA enough to ship commercial products that rely on it. I would be delighted though, if anyone can prove me wrong.
i swear people who are outsiders here have only clickbait takes; if you've never had to ship GPU code professionally you should just not comment on these things.
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.
yeah yeah, "when" an often keyword with AI it seems. As Mr. E. Nigma put it - what always comes but never arrives? Meanwhile the moat deepens and it's build on inertia and laziness and Nvidia knows this really REALLY well.
Oh, absolutely. Nvidia is the modern day “nobody gets fired for buying IBM”. The reason we’re still using Unix is not because it’s the best, but because it had to much inertia to let any alternative become its successor. Similarly, C and HTML are maybe the most terrible yet extremely useful languages we have.
trivial to translate (or transpile) - okay. trivial to understand the result - not so much. trivial to then evolve it - hm... perhaps a different story. still, it seems very likely now, that such "quick rewrites" are viable, not sure if an open approach to them is viable. a newly born open project that was LLM-derived, and not by a credible author, which spans hundreds of files no human eye has ever looked at, can only work for a closed organization, but will never be trusted by the general audience... just like that.
man i can't say how much i used to like cuda when i had a nvidia gpu it made ml so much fun and on amd its a war especially on rdna 2 cards which i have. hopefully one day we will be able to properly translate cuda for its amd counter parts
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[ 47.4 ms ] story [ 680 ms ] threadThe 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.
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.
It's unbearable that most LLM inference happens on closed H/W, closed drivers, and closed SDKs.
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 would really appreciate your input!
My software is labeled "CPU only unless using an nVidia GPU". I would prefer to strikethrough "nVidia". Incidentally, this means no more Mac support.
Source: worked at a bunch of accelerator companies in the kernels or equivalent team. They're nothing alike.
ZLUDA is already doing that, no?
Src: https://github.com/vosen/ZLUDA
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.
https://github.com/Zaneham/Booth
https://scale-lang.com/