Engineer Debugs vLLM PagedAttention KV Cache Corruption That Caused Production Outage

A software engineer responding to an on-call incident discovered that their vLLM-based model serving system had gone down due to KV cache corruption in the PagedAttention layer, with peak request traffic reaching over 14,700 RPS at the time of the alert. Initial debugging attempts — including adjusting timeout settings, restarting Kubernetes pods, and checking GPU health — all failed to resolve the issue, consuming roughly 45 minutes. The root cause ultimately traced back to a single-line fix in the team's Dockerfile. Following the incident, the team added three Grafana-based alerting rules to catch NCCL communication failures and high all-reduce latency before users are impacted in future. The engineer shared the full debugging code and fix on GitHub for others running vLLM with PagedAttention in production environments.