It’d be nice if there was a ‘Geekbench of the servers’ though, it’s nice to have a round number as a *general/relative* comparison that is easily indexed, and has clients for all kinds of different devices/architectures. Not as many runs are shared for the server benchmarks we see on Phoronix, STH, etc.

Most of these servers are going to be used as hosts for a hypervisor. Therefore you are basically only limited on parallel work by your physical core counts. These 128 core CPUs aren’t going to be used for a SQL DB in a physical appliance. However, using Geekbench 6 is akin to doing that. Instead a better way to view it would be to have 10 VMs with 24 vCPUs (12 physical cores) running Geekbench 6 benchmarks on each VM but having them staggered as you almost never are running higher than 50% CPU utilization at any given time. Sure your VM Geekbench score won’t be as high as say a Ryzen 7900X but all 10 of them will look A LOT different than it would as a single 128 core appliance.

Amdahl’s Law limits strong scaling, Gustafson’s Law provides for weak scaling and some tasks are just not parallel. Does lack of scaling in Geekbench 6 result from hardware with catastrophically bottlenecked memory bandwidth or from the limits of strong scaling?

In my experience thrashing the shared L3 cache plays a huge role when scaling embarrassingly parallel but memory intensive tasks on current generation Xeon and Epyc hardware. Therefore, I find performance comparisons to be especially important in contexts where the observed parallel scaling is less than perfect.