  Deep learning recommendation models have grown to the terabyte scale.
Traditional serving schemes--that load entire models to a single server--are
unable to support this scale. One approach to support this scale is with
distributed serving, or distributed inference, which divides the memory
requirements of a single large model across multiple servers.
  This work is a first-step for the systems research community to develop novel
model-serving solutions, given the huge system design space. Large-scale deep
recommender systems are a novel workload and vital to study, as they consume up
to 79% of all inference cycles in the data center. To that end, this work
describes and characterizes scale-out deep learning recommendation inference
using data-center serving infrastructure. This work specifically explores
latency-bounded inference systems, compared to the throughput-oriented training
systems of other recent works. We find that the latency and compute overheads
of distributed inference are largely a result of a model's static embedding
table distribution and sparsity of input inference requests. We further
evaluate three embedding table mapping strategies of three DLRM-like models and
specify challenging design trade-offs in terms of end-to-end latency, compute
overhead, and resource efficiency. Overall, we observe only a marginal latency
overhead when the data-center scale recommendation models are served with the
distributed inference manner--P99 latency is increased by only 1% in the best
case configuration. The latency overheads are largely a result of the commodity
infrastructure used and the sparsity of embedding tables. Even more
encouragingly, we also show how distributed inference can account for
efficiency improvements in data-center scale recommendation serving.
