vLLM vs Ollama: How They Differ and When To Use Them
With Examples of Offline and Online Inference
This article takes a close look at two popular open-source tools for LLM inference: vLLM and Ollama. Both are widely used but optimized for very different use cases.
vLLM is built to maximize GPU throughput in server environments, while Ollama focuses on ease-of-use and local model execution, often on CPU. While they might seem like alternatives at first glance, they serve distinct roles in the LLM ecosystem.
We'll explore how vLLM achieves high performance through low-level memory optimizations like PagedAttention, and how it excels in multi-user or long-context scenarios. Then we'll look at Ollama’s lightweight design, its integration with quantized GGUF models, and its focus on simplicity.
The goal of this article is to help you understand the differences between vLLM and Ollama, so you can choose the right tool based on your use case. You don’t need any prior experience with LLM inference or deployment to understand this article. This is meant to be beginner-friendly, with the exception of the PagedAttention (short) section just ahead.
I prepared a simple notebook containing the main commands to set up and try vLLM and Ollama with Qwen3:
Note: What about SGLang? I’m much less familiar with SGLang, but I think it is as good as vLLM, maybe with fewer features. SGLang can be used for the same use cases as vLLM.