This paper proposes model- and system-level acceleration and optimization strategies to reduce inference latency and increase system throughput in real-time recommendation systems, which have become increasingly important due to the rapid growth of Internet services. Model-level optimizations, such as lightweight network design, structural pruning, and weight quantization, significantly reduce the number of model parameters and computational requirements. System-level performance is enhanced by integrating heterogeneous computing platforms, leveraging high-performance inference libraries, and implementing elastic inference scheduling and load balancing mechanisms based on real-time load characteristics. Experimental results demonstrate a practical solution that reduces latency by less than 30% compared to baselines and more than doubles system throughput while maintaining baseline recommendation accuracy.
