A model file by itself does nothing. The runtime loads it, manages memory, accepts prompts, streams tokens, and exposes the model to a chat app or API.
Choosing a runtime is really choosing how much control you want and how much setup you are willing to carry.
The Friendly Layer
Ollama is often the easiest first step. Install it, pull a model, run a command, and you have a local API. LM Studio is friendly in a different way: it gives you a desktop interface for browsing, downloading, chatting, and serving models locally.
The Engine Room
llama.cpp sits closer to the metal. It is the engine behind a lot of local GGUF workflows and is useful when you want flags, experiments, and direct control. vLLM lives more on the server side, especially when throughput, batching, and multi-user serving start to matter.
The runtime is where the model becomes infrastructure.
Personal setup
Ollama or LM Studio, often with Open WebUI on top for a nicer private chat surface.
Service setup
llama.cpp server, vLLM, containers, private networking, logs, health checks, and access controls.
Start Boring
If you are new, start with Ollama or LM Studio. Once you know what is slow, what breaks, and what you want to automate, the lower-level tools will make more sense.
Do not optimize the engine before you know what you are driving: chat, code, RAG, agents, or a small internal API.