Hacker News Reader

Comment by mattfrommars | Hacker News Reader

Great, I have yet to understand why does not the ML community really push or move away from CUDA? To me, it feel like a dinosaur move to build on top of CUDA which is screaming proprietary nothing about it is open source or cross platform.

The reason why I say its dinosaur is, imagine, we as a dev community continued to build on top of Flash or Microsoft Silverlight...

LLM and ML has been out for quiet a while, with AI/LLM advancement, the transition must have been much quicker to move cross platform. But this hasn't yet and not sure when it will happen.

Building a translation layer on top CUDA is not the answer either to this problem.

View on HN

For me personally, hacking together projects as a hobbiest, 2 reasons :

1. It just works. When i tried to build things on Intel Arcs, i spent way more hours bikeshedding ipex and driver issues than developing

2. LLMs seem to have more cuda code in their training data. I can leverage claude and 4o to help me build things with cuda, but trying to get them to help me do the same things on ipex just doesn't work.

I'd very much love a translation layer for Cuda, like a dxvk or wine equivalent.

Would save a lot of money since Arc gpus are in the bargain bin and nvidia cloud servers are double the price of AMD.

As it stands now, my dual Intel Arc rig is now just a llama.cpp inference server for the family to use.

If CUDA counts as “just works”, I dread to see the dark, unholy rituals you need to invoke to get ROCm to work. I have spent too many hours browsing the Nvidia forums for obscure error codes and driver messages to ever consider updating my CUDA install and every time I reboot my desktop for an update I dread having to do it all over again.

https://github.com/gpuocelot/gpuocelot

https://github.com/vosen/ZLUDA

https://docs.scale-lang.com/

https://github.com/ROCm/HIPIFY

What kind of model learn and what's its token output on intel gpu's?

Except I never hear complaints about CUDA from a quality perspective. The complaints are always about lock in to the best GPUs on the market. The desire to shift away is to make cheaper hardware with inferior software quality more usable. Flash was an abomination, CUDA is not.

Flash was popular because it was an attractive platform for the developer. Back then there was no HTML5 and browsers didn't otherwise support a lot of the things Flash did. Flash Player was an abomination, it was crashy and full of security vulnerabilities, but that was a problem for the user rather than the developer and it was the developer choosing what to use to make the site.

This is pretty much exactly what happens with CUDA. Developers like it but then the users have to use expensive hardware with proprietary drivers/firmware, which is the relevant abomination. But users have some ability to influence developers, so as soon as we get the GPU equivalent of HTML5, what happens?

> users have to use expensive hardware with proprietary drivers/firmware

What do you mean by that? People trying to run their own models are not “the users” they are a tiny insignificant niche segment.

There are far more people running llama.cpp, various image generators, etc. than there are people developing that code. Even when the "users" are corporate entities, they're not necessarily doing any development in excess of integrating the existing code with their other systems.

We're also likely to see a stronger swing away from "do inference in the cloud" because of the aligned incentives of "companies don't want to pay for all that hardware and electricity" and "users have privacy concerns" such that companies doing inference on the local device will have both lower costs and a feature they can advertise over the competition.

What this is waiting for is hardware in the hands of the users that can actually do this for a mass market price, but there is no shortage of companies wanting a piece of that. In particular, Apple is going to be pushing that hard and despite the price they do a lot of volume, and then you're going to start seeing more PCs with high-VRAM GPUs or iGPUs with dedicated GDDR/HBM on the package as their competitors want feature parity for the thing everybody is talking about, the cost of which isn't actually that high, e.g. 40GB of GDDR6 is less than $100.

Maybe the situation has gotten better in recent years, but my experience with Nvidia toolchains was a complete nightmare back in 2018.

The cuda situation is definitely better. The nvidia struggles are now with the higher-level software they’re pushing (triton, tensor-llm, riva, etc), tools that are the most performant option when they work, but a garbage developer experience when you step outside the golden path

I want to double-down on this statement, and call attention to the competitive nature of it. Specifically, I have recently tried to set up Triton on arm hardware. One might presume Nvidia would give attention to an architecture they develop, but the way forward is not easy. For some version of Ubuntu, you might have the correct version of python ( usually older than packaged ) but current LTS is out of luck for guidance or packages.

https://github.com/triton-lang/triton/issues/4978

I think you’ve mixed up your Triton’s; I’m talking about Triton Inference Server from NVIDIA while you’re talking about Triton the CUDA replacement from OpenAI

Reply on news.ycombinator.com