Lit route = live request pathRack = the eight runtimesGreen bar = KV cacheRed lamp = NO FITA telephone switchboard wired to a rack of engines. Every app plugs into the same socket. Watch the trunk cord move when you change OS, audience or format.Apps reach a local model through a runtime, and almost every runtime exposes the same OpenAI-compatible HTTP API (POST /v1/chat/completions), so one app can swap engines without changing code. Desktop-first runtimes put ease first: llama.cpp (GGUF; CUDA, Vulkan, Metal and CPU backends; llama-server), Ollama (a model library and Modelfile on port 11434, GGUF on a llama.cpp-derived engine), LM Studio (a GUI over llama.cpp and MLX engines) and Windows ML, which ships in Windows on ONNX Runtime and picks an execution provider per PC, TensorRT for RTX on NVIDIA. Server-first engines put throughput first: vLLM (PagedAttention, continuous batching), SGLang (RadixAttention prefix cache, structured output), TensorRT-LLM (compiled engines, in-flight batching, FP8 and NVFP4 on Blackwell) and NVIDIA NIM (packaged, optimized microservices). Pick an OS, an audience, a GPU and a model format, and the bay patches the engine that fits. Raise users or context and watch the request bottles queue behind the gate (no free slot, or no memory left for another user's KV cache) or the memory bar spill loose segments into the overflow tray (the model does not fit). The bar is the card's memory in tenths: dark segments are weights plus about 1 GB of runtime overhead, green segments the KV cache of the users being served. Above 32 users each bottle stands for several users; the counters give the exact numbers.