Yes and no. AMD uses different architectures for their consumer grade hardware and their HPC stuff as well (i.e. RDNA vs. CDNA). CUDA support (or rather lack t

Yes and no. AMD uses different architectures for their consumer grade hardware and their HPC stuff as well (i.e. RDNA vs. CDNA). CUDA support (or rather lack thereof, which isn't AMD's fault) plays a major role w.r.t. software support, but AMD's compute architecture isn't DL-focused either. The MI250X compute part for example has a FP16 to FP32 ratio of 8:1 and a FP32 to FP64 ratio of 1:1; in other words it's an absolute beast at GPGPU compute. The 6900XT on the other hand has a FP16 to FP32 ratio of just 2:1 and a FP32 to FP64 ratio of 1:16 (i.e. it's severely restricted at high precision workloads and OK at half-precision). Comparing this to the specs of NVIDIA cards, they're still vastly superior on paper. The consumer versions of Ampere only get 1:1 (FP16:FP32) and 1:64(!!! FP32:FP64) respectively. But then again, NVIDIA cards feature dedicated "tensor cores", which have no equivalent on AMD consumer grade hardware. The main selling point for NVIDIA, however, is software support and mindshare. They started to buy themselves into academia in the late 2000s by sponsoring labs and providing a vast ecosystem of software libraries for deep learning and GPGPU support in general. This not only helped kickstarting the deep learning revolution but also tied their hardware and brand name to GPGPU, which basically became synonymous with CUDA at that point.