Building a Production ML Inference Stack with KServe, vLLM, and Karmada

Your ML models work perfectly in development. The inference latency looks great, the throughput numbers hit your targets, and your team is ready to ship. Then production reality hits: you need to serve this model across three regions, handle failover when a GPU node disappears, and maintain consistent p99 latency for users in Singapore and São Paulo simultaneously. Suddenly you're writing custom health checks, building bespoke routing logic, and wondering why your "simple" deployment turned into a distributed systems research project. The fundamental problem is that ML inference doesn't behave like traditional web services. You can't just throw a load balancer in front of GPU-bound workloads and call it a day. Models have cold-start penalties measured in seconds, not milliseconds. GPU memory fragmentation creates capacity cliffs that don't show up in CPU utilization metrics. And when a node fails, you can't spin up a replacement in the time it takes to serve a single request—model load