Comment by burnte

> Interesting that there's separate inference and training focused hardware. Do companies using NV hardware also use different hardware for each task or is their compute more fungible?

Dedicated hardware will usually be faster, which is why as certain things mature, they go from being complicated and expensive to being cheap and plentiful in $1 chips. This tells me Google has a much better grasp on their stack than people building on NVidia, because Google owns everything from the keyboard to the silicon. They've iterated so much they understand how to separate out different functions that compete with each other for resources.