Complete procedures with a way back.

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.

Compare LM Studio and Open WebUI by role rather than treating them as interchangeable local AI applications. Test LM Studio as a model runtime/API and Open WebUI as a frontend that can connect to it or other backends, including multi-user identity and deployment choices.

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.

Create a small Linux desktop chat application that talks only to LM Studio on loopback, streams answers into a Tk interface, manages model loading explicitly, keeps conversation storage opt-in, and packages the result as a reproducible Python zip application.

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.