production-stack

Why we included this project

Once a single GPU box stops being enough for your vLLM workload, this stack is a tested starting point for cluster-scale serving. It wraps vLLM in request routing, a metrics dashboard, and KV-cache offloading, so moving from one instance to a distributed deployment is mostly a Helm install rather than a custom engineering project. The tutorials walk through AWS, GCP, Azure, and Lambda Labs, which helps if you are new to Kubernetes. As vLLM's own reference implementation, it stays close to the engine's features and gives you a sensible baseline before you hand-roll your own operators and routing layer. It is a deployment framework, not a model or an app, so you bring the weights and decide on your own autoscaling and monitoring.