Infrastructure Stack
Production-grade orchestration patterns for local LLMs.
The GPU is only the engine — production local AI is an infrastructure stack: an inference engine executes the model (llama.cpp, MLX), a runtime makes it usable (Ollama), a serving layer makes it scale (vLLM/TGI, whose paged attention + continuous batching multiply a GPU's throughput 10–20× under concurrency), and around them sit the chat UI (Open WebUI), the vector DB for RAG (Chroma, Qdrant, pgvector) and the ops layer — Docker with GPU passthrough, pinned versions, Prometheus metrics.
The three reference shapes: solo box (Ollama + one GPU, zero ceremony), team server (vLLM + Open WebUI + SSO on a 48–80GB card behind Docker Compose), production fleet (vLLM instances behind a load balancer, k8s, autoscaling, small-model routing). The classic failure is serving a team on a single-user runtime: it queues, and users blame the model.
Deep references: the full software stack · reference architectures · private ChatGPT build. Below: the latest infrastructure intelligence from the radar.
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