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Comment by williamDafoe | Hacker News Reader

AMD's focus was always on pure compute power at a good price. And they always beat NVidia at that game. AMD cards always had the highest hash rate per dollar in crypto mining. AMD has 100% of the console market and the fastest iGPUs by 2x over Intel.

NVidia decided to use gimmicks to sell their cards including texture compression, lighting tricks, improved antique video encoders, motion smoothing, bad proprietary variable refresh rate, ray tracing, cuda and now machine learning features.

Nvidia is fortunate that machine learning has taken off. That is masking AMD winning market share from weak overpriced NVidia 3D products!

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You're mashing together a lot under "gimmicks" there.

Texture compression: Useful for games, ongoing work, although I wish they would make cards with appropriate amounts of VRAM

Lighting tricks: Not sure what this is referencing

Improved antique video encoders: NVENC started out with only h.264, but now it supports h.265 and AV1, which aren't antique at all. Niche, but widely used in the streaming industry.

Motion smoothing: The hardware optical flow accelerators in newer cards are important for DLSS, which is a bit gimmicky but works mostly as advertised.

Bad proprietary vrr: No argument here, gsync sucked.

Ray tracing: All 3d games are going to be ray traced sooner or later. Getting a head start on it is a good move, and it's a big head start. The 4090 is ~100% faster than the 7900xtx.

CUDA: No one can seriously call CUDA a gimmick.

Machine learning features: Tensor cores are great.

CUDA is a gimmick though.

CUDA isn't a "technology", its a shader language that has been supplanted by better industry-wide standards.... the same standards whose shader languages are compiled by the same Nvidia shader compiler.

CUDA is a moat whose muddy waters has long since ran dry, and you're drinking koolaid if you think its still relevant for greenfield projects.

> and you're drinking koolaid if you think its still relevant for greenfield projects.

So I want to start a new GPU compute project today. Obviously this will primarily be deployed to AWS/Azure/etc, which means only high-end GPUs available are Nvidia. What do you recommend developing this application with?

The way I see it, you would have to be drinking koolaid to use anything besides CUDA.

Why do you think I can't use standard APIs on Nvidia? I literally just said same compiler does both; Nvidia sits on the Khronos committee! They co-wrote the API that everyone uses, that their compiler also speaks!

Vulkan Compute is not an alternative to CUDA. There's a reason PyTorch doesn't provide Vulkan in their official binaries. It's in the source, though--build it yourself, try running any recent ML project, and see what happens.

Thats a weird strawman; compute in Vulkan is a replacement compute in OpenGL and legacy D3D, and as a twin sibling to compute in D3D12.

OpenCL is the actual intended replacement for all the pre-standard APIs, and has achieved its goals. If you want SPIR-V IR, OpenCL allows this and all the major vendor impls support it.

CUDA has no equivalent for SPIR-V, and never will. Nvidia's own internal IR is not, and never will be, documented nor stable across driver versions. This is a massive downside for ML middlewares, as they have no way of directly emitting optimal code that cannot easily be represented in the HLSL-flavored syntax in CUDA.

>a shader language that has been supplanted by better industry-wide standards....

Are you talking about Vulkan? If so, I'm not sure 'supplanted' is the right word.

OpenCL.

There exists no AMD alternative to CUDA. How is this a “gimmick”?

> CUDA isn't a "technology", its a shader language that has been supplanted by better industry-wide standards

As someone who uses industry-wide standards in a related field...

The proprietary implementation often has the benefit of several more years of iteration with real products than the open standards. 'Supplanted' can only really be evaluated in terms of popularity, not newness or features, because features on paper aren't features in practice until they pay for their migration cost.

That's a wild perspective. I don't know how you can really come to that conclusion either. One attempt at getting Blender to render something using an AMD vs Nvidia card will paint a very very clear picture.

Calling features which are integral to all modern games and most of which also got adopted by other vendors 'gimmicks' is kind of ridiculous.

You're entitled to your opinion (which I agree with in broad strokes) but with respect, the op article is specifically about ML. Calling cuda a "gimmick" is silly and completely underestimating the datacenter/ML cluster market share (it dwarfs consumer GPU), and fact of the matter is AMD's CUDA equivalent segfaults. So if "being actually usable to the biggest market" is a gimmick, so be it.

> AMD has 100% of the console market

thats not true, since the switch is based on an nvidia platform. since it's still 1/3 of the market, it's not as bad as it used to be.

and yet amd lately has been quietly just been slightly less than nvidia but worse product. amd sucks thats just it. their market share is crumbling and nvidias is getting stronger because people are like fuck it, at that price might as well jsut buy the better one that Just works TM

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