I've used DSPs, custom boards with compute hardware (FPGA image processing), and various kinds of GPUs. I would have a very hard time trying to point to ways in which the NVIDIA toolkit could be compared to what's out there and not come away with a massive sense of relief. For the most part 'it just works', the models are generic enough that you can actually get pretty close to the TDP on your own workloads with custom software and yet specific enough that you'll find stuff that makes your work easier most of the time.

I really can't complain, now, FPGAs, however... And if there ever is a company that comes out and improves substantially on this I'll be happy for sure but if you asked me off the bat what they should improve I honestly wouldn't know, especially not taking into account that this was an incremental effort over ~2 decades and that originated in an industry that has nothing to do with the main use case today and some detours into unrelated industries besides (crypto, for instance).

From fluid dynamics, FEA, crypto, gaming, genetics, AI and many others with a single generic architecture and delivering very good performance is no mean feat.

I'd love to hear in what way you would improve on their toolset.