Large Language Model (LLM) inference, which involves generating output from trained models, is essential for producing the conversational and user-friendly interactions that have made LLMs increasingly popular in both consumer and enterprise contexts. As their usage grows, managing costs, reducing latency, and optimizing throughput have become crucial challenges. While cloud-based LLM services such as those from OpenAI do not require user-side optimization, organizations deploying their own models like LLAMA or Gemma need to experiment with various optimization techniques to enhance performance. This article, part of a broader series on AI application development, aims to delve into the specifics of optimizing LLM inference, offering practical guidance on experimentation and performance measurement.