Complete procedures with a way back.

Compare Ollama and vLLM without confusing a convenient local workstation runtime with a throughput-oriented serving engine. Reproduce model, request, hardware, batching, concurrency, multi-GPU, API, security, and operations evidence before choosing.

Build a governed supervised fine-tuning pipeline for a small open-weight causal model: prove base and dataset licenses, audit immutable splits, establish a frozen baseline, train a LoRA or QLoRA adapter, resume checkpoints, evaluate regressions, merge deliberately, test post-training quantization, and serve a pinned adapter through an authenticated API.

Deploy a pinned vLLM release behind a trusted TLS and authentication proxy, size GPU and KV-cache capacity from real workloads, isolate artifacts and listeners, verify quality and overload behavior, monitor the engine, and rehearse complete-bundle rollback.

Build a bounded, repeatable compatibility suite for Ollama 0.32.0, LM Studio 0.4.1, and vLLM 0.23.0. The guide tests advertised models, non-streaming and streaming chat, strict structured output, forced tool calls, and the Responses endpoint while keeping provider documentation, locally measured evidence, and product positioning visibly separate.