Actual article title says "won't"; wont is a word meaning habit or proclivity.

Why can't it be done w/ AI? Why does it need to be maintained w/ manual programming? Take the ROCm specification, take your CUDA codebase, let one of the agentic AIs translate it all into ROCm or the AMD equivalent.

Are the hyperscalers really using CUDA? This is what really matters. We know Google isn't. Are AWS and Azure for their hosting of OpenAI models et al?

Let’s just say what it is: devs are too constrained to jump ship right now. It’s a massive land grab and you are not going to spend time tinkering with CUDA alternatives when even a six-month delay can basically kill your company/organization. Google and Apple are two companies with enough resources to do it. Google isn’t because they’re keeping it proprietary to their cloud. Apple still have their heads stuck in sand barely capable of fixing Siri.

I agree pretty strongly. A translation layer like this is making an intentional trade: Giving up performance and HW alignment for less lead time and effort to make a proper port.

Perhaps I'm misunderstanding the market dynamics; but isn't AMDs real opp inference over research?

Training etc still happens on NVDA but inference is somewhat easy to do on vLLM et al with a true ROCm backend with little effort?

Yeah, ROCm focused code will always beat generic code compiled down. But this is a really difficult game to win.

For example, Deepseek R-1 released optimized for running on Nvidia HW, and needed some adaption to run as well on ROCm. This was for the exact same reasons that ROCm code will beat generic code compiled into ROCm, in the same way. Basically the Deepseek team, for their own purposes, created R-1 to fit Nvidia's way of doing things (because Nvidia is market-dominant) on their own. Once they released, someone like Elio or AMD would have to do the work of adapting the code to run best on ROCm.

For more established players who weren't out-of-left-field surprises like Deepseek, e.g. Meta's Llama series, mostly coordinate with AMD ahead of release day, but I suspect that AMD still has to pay for the engineering work themselves while Meta does the work to make it run on Nvidia themselves. This simple fact, that every researcher makes their stuff work on CUDA, but AMD or Elio has to do the work to move it over to get it to be as performant on AMD, is what keeps people in the CUDA universe.