>For desktop devices "instructions per dollar" would be most relevant. Well, it's one measure, but scaling potential and absolute performance limit matters as

>For desktop devices "instructions per dollar" would be most relevant. Well, it's one measure, but scaling potential and absolute performance limit matters as well. In use cases where a desktop is performing work to help one or more humans, the cost of salary and other support may utterly drown out even a very expensive extremely high power system. Ie., a 1200W workstation being used at maximum power 10 hours a day 365 days a year (an absurd utilization ratio) at $0.15/kWh (average US electricity cost) would still only be around $650/year. If it boosted the productivity of a typical tech worker even 1% it'd pay for itself no problem. It's easy to lose sight sometimes of how historically incredible the bang for the buck is in tech. I think it's important to remember because some designs that do incredibly well at small sizes run into challenges scaling. Like with CPUs, the nature of silicon fabrication makes it ever more difficult to grow a monolithic die. Switching to a chiplet-based design does mean absolute efficiency and minimum power challenges amongst others, but dramatically improves scalability at the high end. It's a sort of infrastructure tradeoff. Apple for example has done incredibly well with its big silicon SoCs from handhelds to portable Macs, but it's been struggling to do a Mac Pro or even updated Studio. It's not clear that they physically can do something on the level of a modern Epyc chip anymore than Intel could with monolithic Xeons. So normalized performance/watt and performance/$ and so on do matter, but absolute final density and scale up are another part of the matrix for certain use cases too.