A practical guide to deploying distributed AI inference using vLLM and llm-d across six traffic-shaped blueprints: high-concurrency chat, long-context RAG, high-throughput batch, distributed AI-grid (Model-as-a-Service), hybrid sovereign-to-cloud-burst, and edge inference on workstation GPUs. Each blueprint covers workload signature, topology, key mechanisms (prefill/decode disaggregation, KV-cache tiering, speculative decoding, model cascading), and cost shape. The post also provides inference troubleshooting recipes for TTFT/TPOT regressions using vLLM Prometheus metrics and NVIDIA Nsight tools, and closes with a four-step scaling roadmap from a single vLLM instance to a full distributed AI grid on Red Hat OpenShift AI.
Table of contents
Deployment blueprints by traffic shapeInference troubleshooting recipesA step-by-step roadmap for scaling AI inference deploymentsNext steps for scaling your distributed inference workloads57 Impressions