Serving frameworks are essential tools in the deployment of machine learning models, particularly in the context of large language models (LLMs). They facilitate the process of making these models accessible for inference, allowing applications to utilize their capabilities in real-time. By providing a structured environment, serving frameworks help manage the complexities of model deployment, including scaling, resource allocation, and integration with various data sources. Different frameworks offer unique features tailored to specific use cases, enabling developers to choose the most suitable option based on their performance needs and operational requirements. Understanding these frameworks is crucial for effective model serving.