This work addresses the scalability and efficiency of RAM-based storage systems wherein multiple objects must be retrieved per user request. Here, much of the CPU work is per server transaction, not per requested item. Adding servers and spreading the data across them also spreads any given set of requested items across more servers, thereby increasing the total number of server transactions per user request. The resulting poor scalability, dubbed the Multi-get Hole, has been reported in Web 2.0 systems using memcached - a popular memory-based key-value storage system. We present Replicate and Bundle (RnB), a somewhat unintuitive approach: rather than add CPUs, we add memory. Object replicas are mapped “randomly” to servers, and requested objects are bundled, selecting replicas so as to minimize the number of servers accessed per user request and thus the total CPU work per request. We studied RnB via simulation in the context of DRAM-based storage, utilizing micro benchmarks and implemented RnB modules for calibration. Our results show that RnB substantially reduces the number of transactions per request, making operation more efficient. Also, unlike most alternatives, RnB permits flexible growth and relatively easy deployment. Finally, in systems wherein data is replicated for other reasons, RnB is nearly free.