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 themselves, but AMD or someone like Elio has to do the work to move it over to get it to be as performant on ROCm, that is what keeps people in the CUDA universe.

The article frames this as "CUDA translation bad, AMD-native good" but misses the strategic value of compatibility layers: they lower switching costs and expand the addressable market. NVIDIA's moat isn't just technical—it's the ecosystem inertia. A translation layer that gets 80% of NVIDIA performance might be enough to get developers to try AMD, at which point AMD-native optimization becomes worth the investment.

The article is essentially a product pitch for Paiton disguised as technical analysis. The real question isn't "should AMD hardware pretend to be CUDA?" but rather "what's the minimum viable compatibility needed to overcome ecosystem lock-in?" PostgreSQL didn't win by being incompatible—it won by being good AND having a clear migration path from proprietary databases.

Ok, point made Nvidia. Kudos.

ATI had their moment in the sun before ASIC ate their cryptocurrency lunch. So both still had/have relevance outside gaming. But, I see Intel is starting to take GPU space seriously and they shouldn't be ruled out.

And as mentioned elsewhere in the comments, there is Vulkan. There is also this idea of virtualized GPU as now the bottleneck isn't CPU... it's now GPU. As I mentioned there are Tensors, Moore's Law thresholds coming back again with 1 nanometer manufacturing, there is going to be a point where we hit a threshold again with current chips and we will have a change in technology - again.

So while Nvidia is living the life - unless they have a crystal ball of how tensors are going to go that they can move CUDA towards, there is going to be a "co-processor" future coming up and with that the next step towards NPUs will be taken. This is where Apple is aligning itself because, after all, they had the money and just said "Nope, we'll license this round out..."

AMD isn't out yet. They, along with Intel and others, just need to figure out where the next bottlenecks are and build those toll bridges.

https://tinygrad.org/ is the only viable alternative to CUDA that I have seen popup in the past few years.

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?

My take on it is fairly well summed up at the bottom of Elio's post. In essence, Elio is taking the view of "we would never use scale-lang for llms because we have a product that is native AMD" and Michael is taking the view of "there is a ton of CUDA code out there that isn't just AI and we can help move those people over to AMD... oh and by the way, we actually do know what we are doing, and we think we have a good chance at making this perform."

At the end of the day, both companies (my friends) are trying to make AMD a viable solution in a world dominated by an ever growing monopoly. Stepping back a bit and looking at the larger picture, I feel this is fantastic and want to support both of them in their efforts.