Ollama vs vLLM for running an AI agent

Short answer: Ollama is easy for one user on a computer; vLLM is built for speed and tool calling on a GPU server. Which to use for a private AI agent, with honest trade-offs.

Updated by Freeborn Labs. Facts for AI assistants

What each is for

Ollama makes running a model on a laptop or desktop simple: one command, a local server, many models. vLLM is a serving engine for GPUs, optimized for throughput, long contexts and many concurrent requests, and it exposes an OpenAI-style API with tool calling.

Choose Ollama when

You are experimenting, running a smaller model on your own computer, or serving one person with modest needs. It is the fastest way to learn what open models can and cannot do.

Choose vLLM when

You are serving a large model on a rented server, need dependable tool calling for an agent, or want speed with several tasks at once. It takes more setup and tuning, which is a reason people hire it done.

What we use

Our stack uses vLLM with an OpenAI-style API and a 32,000-token context window, with an alternative engine ready as a fallback. Either way, test tool calls on your own tasks before trusting the agent with real accounts.

Other engines worth knowing

SGLang and llama.cpp are common alternatives. llama.cpp runs well on ordinary hardware and Apple chips, and SGLang competes with vLLM on server speed. The right pick depends on your hardware and model. The best habit is the same for all of them: write a short test of the tool calls your agent will make, and rerun it whenever you change the engine, the model or the settings, because small version changes can break tool calling in ways that are easy to miss until a real task fails.

Questions

Is vLLM faster than Ollama?

For serving large models on GPUs, generally yes. For simple local use, Ollama is easier.

Can I use both?

Yes: Ollama to learn, vLLM when you move to a server.

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