The Radeon VII is special compared to most older (and current) affordable GPUs in that it used HBM giving it memory bandwidth comparable to modern cards ~1TB/s and has reasonable FP64 (1:4) throughput instead of (1:64). So this card can still be pretty interesting for running memory bandwidth intensive FP64 workloads. Anything affordable afterward by either AMD or Nvidia crippled realistic FP64 throughput to below what a AVX-512 many-core CPU can do.
On the other hand, for double precision a Radeon Pro VII is many times faster than a RTX 4090 (due to 1:2 vs. 1:64 FP64:FP32 ratio).
Moreover, for workloads limited by the memory bandwidth, a Radeon Pro VII and a RTX 4090 will have about the same speed, regardless what kind of computations are performed. It is said that speed limitation by memory bandwidth happens frequently for ML/AI inferencing.
Even the single precision given by the previous poster is seldom used for inference or training.
Because the previous poster had mentioned only single precision, where RTX 4090 is better, I had to complete the data with double precision, where RTX 4090 is worse, and memory bandwidth where RTX 4090 is the same, otherwise people may believe that progress in GPUs over 5 years has been much greater than it really is.
Moreover, memory bandwidth is very relevant for inference, much more relevant than FP32 throughput.