NP hard problems are solvable (in the theoretical sense) by definition, the problem lies in their resource requirements (running time, for the usual complexity classes) as defined in relation to a UTM. (You know this, just establishing a basis.)

The assumption that the universe can be perfectly described by a computable model is satisfied just by a theoretical computational description existing, it says nothing about tractability (running times) and being able to submerge complexity classes in reality fluid or having some thoroughly defined correspondence (other than when we build hardware models ourselves, for which we define all the relevant parameters, e.g. CPU clock speed).

You may think along the lines of "if reality could (easily) solve NP hard problems for arbitrarily chosen and large inputs, we could mimick that approach and thus have a P=NP proving algorithm", or something along those lines.

My difficulty is in how even to describe the "number of computational steps" that reality takes - do we measure it in relation to some computronium-hardware model, do we take it as discrete or continuous, what's the sampling rate, picoseconds (as CCC said further down), Planck time intervals, or what?

In short, I have no idea about the actual computing power in terms of resource requirements of the underlying reality fluid, and thus can't match it against UTMs in order to compare running times. Maybe you can give me some pointers.