Once these components are in place, more complex LLM challenges will require nuanced approaches and considerations--from infrastructure to capabilities, risk mitigation, and talent. Inferencing with traditional ML models typically involves packaging a model object as a container and deploying it on an inferencing server. As the demands on the model increase--more requests and more customers require more run-time decisions (higher QPS within a latency bound)--all it takes to scale the model is to add more containers and servers. But hosting LLMs is a much more complex process which requires additional considerations. LLMs are comprised of tokens--the basic units of a word that the model uses to generate human-like language.