CUDA Lock-In Pressure Finds No Easy Exit
The thread on running CUDA on non-Nvidia hardware (48903715) is a useful reality check on the 'just use alternatives' narrative. Commenters walked through ZLUDA (open source, works on pre-compiled binaries), HIP, SYCL, and Vulkan compute, but kept hitting the same wall: most 'alternatives' target CUDA C++ and completely miss the broader CUDA ecosystem, which includes libraries, tooling, and runtime behaviors that competitors have not replicated.
The most pointed comment: 'Its easier to just get rid of your legacy code entirely and use Vulkan for compute, or have your compiler emit SPIR-V directly. No reason to tie yourself to Nvidia's moat.' That sounds clean until you realize 'get rid of your legacy code entirely' is not a weekend project for teams running serious ML workloads. The practical advice was bleak: the escape hatch exists in theory but the cost of using it is high.
This thread also connected, implicitly, to the broader AI infrastructure story. Every startup building on GPU compute is either paying Nvidia rates or taking on technical debt to avoid them. Neither option is obviously better right now.
So what?
If your product depends on GPU compute, you have a vendor dependency that is very hard to route around without significant reengineering. Start tracking SPIR-V and Vulkan compute maturity now, not when your Nvidia costs become unbearable. The switching cost only grows as your codebase does.