Optimizing model serving for performance

The effective deployment of LLMs in production environments demands meticulous attention to the architecture, performance tuning, and emergency procedures of the serving infrastructure. This section covers the nuances of optimizing model serving for performance, ensuring that LLM applications are fit to serve inferences in a performant way.

Let’s review the types of model deployment to understand their serving performance implications better.

Comparing serverless, containerized, and microservices architectures

Serverless architecture, by design, removes the need for developers to manage server infrastructure, focusing instead on code development. This model, which adjusts computing resources based on incoming request volume, is particularly cost-effective for applications with variable demand, aligning well with the sporadic usage patterns often seen with LLMs.

Before deployment, LLMs often undergo a process of model optimization...