The article outlines strategies for optimizing machine learning model inference services using Python, FastAPI, and PyTorch, emphasizing efficiency improvements through various techniques. Initially, a baseline inference server using default configurations achieved a modest throughput of six predictions per second. By implementing optimizations such as adjusting PyTorch and FastAPI configurations, utilizing Gunicorn for parallelism, and exploring model distillation and quantization, the throughput increased significantly. These changes boosted performance by enabling up to 68 predictions per second, reducing latency to about 60 milliseconds. Additionally, the article highlights the impact of hardware improvements, noting that newer Intel CPUs with Deep Learning Boost can further enhance throughput and latency. The results demonstrate a tenfold increase in throughput and a fivefold reduction in latency, offering insights into maximizing the efficiency of NLP model deployments.