I don't really see it worth mentioning CUDA. It does nothing that any other compute API uses, and the majority of enterprise compute software doesn't use it an

I don't really see it worth mentioning CUDA. It does nothing that any other compute API uses, and the majority of enterprise compute software doesn't use it and/or works on ROCm HIP with minimal performance loss. A lot of research projects (such as all the early LLM research, given the topic) are written in Python and use libraries to shim all of that as well; PyTorch and ONNX both run natively on AMD and is covered under AMD's commercial support. And then we come to the case of llama.cpp, which supports more APIs than any other inference engine... not only does it run on Nvidia/CUDA, it runs on AMD/HIP, Vulkan on at least 4 different vendors, SYCL on at least Intel ARC, BLAS/BLIS, Apple/Metal, Snapdragon's quasi-NPU, and Moore Threads (that new Chinese startup for domestic GPUs). There is no reason to write greenfield code with CUDA today, and most people aren't.