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.

Install and compare Ollama and LM Studio on one workstation using the same model, quantization, prompt, context, hardware, and bounded API harness. Separate GUI convenience, model management, GPU support, privacy, and headless automation from measured latency.

Run one pinned Qwen3-4B Q4_K_M GGUF through llama.cpp, Ollama, and LM Studio on the same machine, normalize the prompt and generation contract, measure cold and warm behavior, blind-score identical outputs, and choose a runner from control, automation, UX, operations, and workload evidence rather than reputation.

Build a deterministic TypeScript router that prefers local Ollama, permits OpenAI cloud only for eligible data with explicit per-request approval, checks provider capabilities and application-level validation evidence, fails over without loops or silent egress, and records content-free HMAC audit events.

Deploy an authenticated loopback service that uses Ollama through its OpenAI-compatible API, exposes only three compiled read-only host probes, records content-free audit evidence, runs under a hardened systemd account, and supports controlled backup, upgrade, rollback, and removal.

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.

Run authenticated local AI behind TLS with private Ollama networking, persistent storage, controlled models, staged updates and tested backup/restore.