What does this equation mean?

where $P_{\mathrm{shared}}$ is the always-active shared-expert capacity, $P_{\mathrm{expert}}$ is the parameter count of one routed expert, and k is the number of routed experts activated per token. Total capacity scales with the number of experts E available to the router, roughly $P_{\mathrm{total}}$ $\approx$ $P_{\mathrm{shared}}$ + E $\cdot$ $P_{\mathrm{expert}}$ , while inference compute scales with $P_{\mathrm{active}}$ , not $P_{\mathrm{total}}$ . This is the exact mechanism behind Meta’s headline figures: Maverick’s 400 billion total parameters and 17 billion active parameters are not two different measurements of the same quantity, they are $P_{\mathrm{total}}$ and $P_{\mathrm{active}}$ under a router with 128 available experts and a small k , and the entire commercial argument for the architecture — a much larger knowledge store served at close to small-model inference cost — depends on that gap holding up under real traffic rather than only in the launch announcement.