Write a Kubernetes YAML configuration to auto-scale an LLM inference service based on traffic load

Question

Can you tell me how to Write a Kubernetes YAML configuration to auto-scale an LLM inference service based on traffic load.

Answer 1

You can auto-scale an LLM inference service in Kubernetes by configuring a HorizontalPodAutoscaler based on CPU or custom metrics.

Here is the code snippet below:

In the above code, we are using the following key points:

• A Deployment manages the lifecycle of LLM inference pods and defines their CPU resource requirements, including requests and limits.

• A HorizontalPodAutoscaler (HPA) dynamically scales the number of pods between 2 and 10.

• CPU utilization is used as the scaling metric, targeting 70% average usage.

Hence, this configuration ensures scalable LLM inference aligned with real-time load.