After training, aligning, and optimizing a large language model, the subsequent engineering task is to deploy it effectively so applications can utilize its capabilities. Serving models with billions of parameters introduces specific challenges related to hardware utilization, latency, throughput, and operational stability. Simply having a trained model artifact is insufficient; a dedicated infrastructure and set of strategies are necessary to handle real-world usage patterns efficiently.

This chapter addresses the practical aspects of deploying LLMs into production environments. You will learn about:

• Designing suitable Application Programming Interfaces (APIs) for interaction with the model.
• Using specialized model serving frameworks like NVIDIA Triton Inference Server and TorchServe to manage deployment.
• Implementing techniques to handle concurrent user requests and optimize resource usage.
• Strategies for load balancing inference requests across multiple model replicas or hardware accelerators.
• Approaches for monitoring key metrics such as latency, throughput, cost, and system health for deployed LLMs.

We will examine the software and infrastructure components required to build reliable and scalable serving systems capable of handling the demands of large language models.