Maybe someone knows what order of cost a release like this requires in real terms of hardware and energy spend?

The cost over the diff from the moment of beginning training to the end. It's iterative from a prior model so it starts there and becomes this release after spending occurs I presume.

For example, is liquid spending millions in energy in this case and the cost is amortized over successive training runs over the initial datacenter spend? What if it's just eating cloud costs and has no dedicated hardware, that sort of thing. I don't particularly mean this model and liquid specifically. Just for this class of smaller fast on-device range of model what is the order of the training cost in real terms?

Maybe its short sighted to ask it as if it's a singular training run and is perhaps the end state of many variations of testing thus all of the R&D training runs would have to be included to to inform what became the final release. Seems like this should be taken into consideration. If anyone happens to know it'd be appreciated!

Training a mid-size model like this can take anywhere between $500k to like $5m. Liquid is spending money on GPU-hours (not sure if you'd call that "energy"). The amortization question is harder to answer without knowing their financials.

The license doesn't really make much sense to me, because if you're already at $10m revenue, you can probably train your own (better, more tailor-suited) model, or at the very least raise money to do so.