In practice, for our service, most of our CPU time is not spent in data mutation, but rather networking and serialization (this is btw, the same conclusion Redi

In practice, for our service, most of our CPU time is not spent in data mutation, but rather networking and serialization (this is btw, the same conclusion Redis came to when they added "multi-threading".) You can scale-out by running multiple instances of the service (shared-nothing, N many depending on how cores you want to run on.) Or, you can do message-passing between cores. In this case, we have 2 modes of scale-up/out (add more nodes to the cluster, or add more shared-nothing LRU caches that are partitioned internally that the process runs, allowing for more concurrency). We however only run one LRU per node, as it turns out that the expensive part is not the bottleneck here, nor will it probably ever be.