What does this equation mean?

Its footprint follows directly from the shape of the cache. For a model with L layers, $h_{kv}$ key-value attention heads, head dimension $d_h$ , holding a sequence of length s across b concurrently served sequences, stored at p bytes per element, the total resident size is $M_{\mathrm{kv}} = 2\,L\,h_{kv}\,d_h\,s\,b\,p$. with the factor of two accounting for storing both keys and values. Two things follow immediately from this equation, and both matter more than the equation’s arithmetic itself. It scales linearly with context length, so a conversation twice as long holds twice the cache. And it scales linearly with the batch b — the exact quantity the previous section identified as the only lever available to make a memory-bound decode step efficient. The key-value cache therefore competes directly, in the same pool of device memory, with the batch size continuous batching is trying to grow. It is not a side cost of serving; it is the resource that sets the ceiling on the technique the previous section depends on.