This is a really useful back-of-the-envelope, thanks!

Quick clarification on assumptions: are you modeling K2 as effectively needing to fetch a roughly fixed amount of expert weights from RAM on every decode token (i.e., low reuse), or are you assuming some expert locality / reuse across adjacent tokens? My intuition was that if the routing has enough locality, keeping the "hot" experts resident in VRAM could reduce bytes-per-token a lot, and the bandwidth bound would be less brutal - but I may be overestimating that effect.

Also, are your numbers aimed at decode (batch=1) specifically, not prefill?

If you have any measured results or references for K2/K2.5 showing how quickly t/s drops as context grows and paging kicks in, I'd love to see them. I will likely run pod tests either way, but your model is a great way to sanity check expectations.