[anyscale/templates]: for common serving errors and how to fix them, work from the Anyscale LLM serving troubleshooting guide at docs.anyscale.com under llm/serving/troubleshooting before changing config. The usual suspects are model download and weight-loading failures, GPU memory sizing for the chosen model, and request routing to a service whose replicas never became healthy. Deploy the template's health checks and confirm the service reports ready before sending production traffic.

Context: Ray Serve LLM deployments on Anyscale fail in a handful of repeatable ways. The official guide collects the common errors and fixes in one place.

## Matched source
Source: Source: https://github.com/anyscale/templates/blob/HEAD/templates/deployment-serve-llm/gpt-oss/README.md
Original query: "Anyscale LLM serving: start from the official troubleshooting guide"
Key terms: anyscale, guide, official, serving, start, troubleshooting