ASICs aren't magic - they are just chips designed to do a single function fast (e.g run a crypto mining algorithm) as an alternative to using a general purpose CPU/GPU whose generality comes at the cost of some performance overhead.
If your application calls for generality - like a gaming card's need to run custom shaders, or an ML model's need to run custom compute kernels, then an ASIC won't help you. These applications still need a general purpose processor, just one that provides huge parallelism.
It seems you may be thinking that all an ML chip does is matrix multiplication, and so a specialized ASIC would make sense, but that's not the case - an ML chip needs to run the entire model - think of it as a PyTorch accelerator, not a matmul accelerator.
Finally, the market for consumer (vs data center) ML cards is tiny relative to the gaming market, and these chips/cards are expensive to develop. Unless this changes, it doesn't make sense for companies like NVIDA to develop ML-only cards when with minimal effort they can leverage their data center designs and build dual-use GPU/compute consumer cards.
If your application calls for generality - like a gaming card's need to run custom shaders, or an ML model's need to run custom compute kernels, then an ASIC won't help you. These applications still need a general purpose processor, just one that provides huge parallelism.
It seems you may be thinking that all an ML chip does is matrix multiplication, and so a specialized ASIC would make sense, but that's not the case - an ML chip needs to run the entire model - think of it as a PyTorch accelerator, not a matmul accelerator.
Finally, the market for consumer (vs data center) ML cards is tiny relative to the gaming market, and these chips/cards are expensive to develop. Unless this changes, it doesn't make sense for companies like NVIDA to develop ML-only cards when with minimal effort they can leverage their data center designs and build dual-use GPU/compute consumer cards.