Training cost is directly impacted by inference cost nowadays. Most of the gains come from RL these days, and that is highly dependant on inference (~7:1 inference:training in units of compute). That's mainly because you want many roll-outs for each training scenario.

Of course inference efficiency is dictated by model architecture, size, etc. You can still guesstimate some of those and have an idea about cost/serve at several size tiers.

That isn't really relevant to GP's point. We still don't know what training costs because we don't know how much RL is done.