The dual-use of GPUs is what's driving Nvidia's demand, for the most part. They bet big on more outlandish designs (eg. CUDA/Tensor cores) and mixed-precision c

The dual-use of GPUs is what's driving Nvidia's demand, for the most part. They bet big on more outlandish designs (eg. CUDA/Tensor cores) and mixed-precision computation, and did a lot of the hardware/software work to get it shipped out years ago. There wasn't much interest in CUDA outside niche research/industry applications for a while, it's a small miracle they kept it around long enough to see the crypto and AI booms. Now their bet is paying big dividends, and other companies are trying to figure out fast ways to leverage raster compute for general-purpose acceleration. Apple initially developed OpenCL with Khronos as a similar-ish analog for GPU acceleration a while ago. There were a few partners that invested in it, but it suffered the same lack of demand as CUDA and languished for a while. Now Apple doesn't support OpenCL, so the purpose of a multi-platform acceleration library has kinda been scuttled. Nvidia played their cards well, and their competitors are going to feel the pain for a while unless they work together again.