Large Language Models are increasing in popularity and their deployments on Kubernetes have steadily increased. LLM applications bring new usage patterns that the industry does not have the expertise in. At the same time, there is a lack of observability in these deployments which makes it difficult to debug performance issues. We will present an end to end walkthrough of how you can leverage client and server LLM observability using Open Telemetry based on the recent efforts in the Kubernetes and Open Telemetry communities to standardize these across LLM clients and model servers. We will also demonstrate how to troubleshoot a real-world performance issue in your LLM deployment and how to optimize your LLM server setup for better performance on Kubernetes.

We'll show how to use Kubernetes autoscaling based on custom model server metrics and demonstrate how they offer a superior alternative to using GPU utilization metrics for such deployments.