A frontier lab optimizes an absolute target and has to explore. A challenger optimizes a distance to the leader and can exploit. That is the whole cost asymmetry, and it shows up in GPU hours and in a bike race.
I used whisper.cpp on an RTX 5070 to transcribe about 100 hours of podcasts in roughly three hours, then turned the Markdown output into a searchable prompt knowledge base.
Most AI infrastructure teams spend 35-60% more on GPU compute than they need to. The cause isn’t cloud pricing. It is architecture, and it is fixable.
GPUs are no longer the bottleneck. Data movement is. I recently attended an online talk that stayed with me longer than most.Not because of a new GPU announcement, but because it clearly articulated something I have seen repeatedly over the years, across very different systems. That message, strongly emphasized by Read more