The document discusses optimizing deep learning models for large-scale production use. It describes a framework that supports pluggable runtimes and hardware like CPUs, GPUs, and FPGAs. Several techniques for optimizing models are presented, such as operation fusing, affinity scheduling, and cache-aware partitioning. Case studies show latency reductions of over 10x and throughput increases of over 100x for various applications after optimization.