No, GPUs are not what you'd design for neural networks from first principles. They were adopted for that because they offered far more parallelism than general purpose cpus, not because they're ideal. That's why Google et all designed TPUs that have a very different internal structure.

Most TPU designs have been based around systolic arrays, which for matrix ops have a quadratic speedup. A typical design is a 128x128 array of MAC units. You shift weights along one dimension, parameters along the other. It takes 128 cycles to shift a full matrix input in, then 128 cycles to shift the answer back out, but during those 256 cycles you got 16,384 MAC operations done, for a factor of 64 speedup.

The other big appeal of this design is it's way simpler than GPUs. The memory access patterns are predictable, there's no threads or thread divergence, etc. So it can be way more efficient in silicon, not just in area but especially in power efficiency.

There's other ideas for architectures besides this basic systolic array idea. If you want to learn about them, a good place would be the HotChips presentations of the last few years: https://hc2025.hotchips.org and similar domain names for prior years.

That’s not what Nvidia is doing.

AMD originally went all in on what you call GPU. It was great for gaming. Not as much for inference.

Nvidia whilst still making it GPU tuned the architecture for AI workloads. Gaming hasn’t improved as much lately.