However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption.
these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware.