vLLM vs Ollama: Which LLM Inference Tool Should You Use?
GPU throughput and multi-user serving vs one-command local GGUF models + practical setup with Qwen3.
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.
vLLM vs Ollama: One for the GPU, the Other for the CPU?
vLLM: Leveraging the GPU at Almost Full Capacity
vLLM is a high-performance open-source library for LLM inference and serving, originally developed at UC Berkeley.
Originally, its main innovation was PagedAttention, a custom memory-management algorithm for attention that treats the GPU memory like virtual memory pages. Instead of allocating one big contiguous chunk for the attention key-value cache (which can lead to 60–80% memory waste due to fragmentation), PagedAttention breaks the cache into fixed-size blocks or “pages”. These pages can be flexibly assigned and reused, dramatically reducing memory overhead.
Now, PagedAttention is implemented in most inference frameworks. vLLM implements many more optimizations on top of it to be even more efficient.
By better exploiting GPU memory, vLLM can batch more concurrent requests and generate multiple tokens in parallel without running out of memory. The result is state-of-the-art throughput.
In short, vLLM is designed for speed and efficiency in serving LLMs, especially when handling many requests or long context lengths. It also provides an easy Python API and an OpenAI-compatible server mode, making integration with applications straightforward.
Ollama: Simple Local Inference
Ollama, on the other hand, is an open-source tool that focuses on simplicity and local model management.
GitHub: https://github.com/ollama/ollama
Ollama stands out for its simplicity and user-friendly design. Unlike vLLM, it doesn’t require technical expertise to get started, making it far more accessible to a wider audience. This ease of use has been a major driver of its popularity: on GitHub, Ollama has earned over 100,000 more stars than vLLM.
It allows you to run LLMs on your local machine (Linux, macOS, or Windows) with minimal setup. Ollama packages model weights, tokenizer, and configuration together into a single bundle (defined by a “Modelfile”). It’s based on llama.cpp, which is optimized for CPU inference. So it easily supports all the models that are supported by llama.cpp and that can be converted to GGUF.
Using a Docker analogy, if a model were a container image, Ollama would be your docker pull and docker run for LLMs.
All you need is a one-line command to install Ollama and another to download a model. Under the hood, Ollama handles downloading the model (often in a quantized format optimized for local inference) and spins up a local inference server.
Ollama provides a REST API (including an OpenAI-compatible /v1/chat/completions endpoint) out-of-the-box. This means you can chat with a model interactively in your terminal or programmatically via HTTP requests, all without relying on external cloud APIs.
When to Use vLLM
Choose vLLM if you are aiming to deploy LLMs in a production or research setting where throughput and latency are critical. For example, if you want to serve a Qwen3 model to multiple users (or handle many parallel requests), vLLM’s continuous batching and optimized GPU utilization will be optimal. For The Kaitchup, when I need results for articles, I use vLLM.
The engine was literally designed to keep a GPU fed with as many tokens as possible at all times, which is ideal for an online service. Moreover, if your use case involves long prompts or outputs (tens of thousands of tokens), vLLM is well-suited.
Another reason to use vLLM is when you need tight integration with a Python ecosystem or custom logic. Because vLLM is a Python library, you can load a model and generate text in a script or notebook, mixing it with other Python code (for example, pre- or post-processing the prompts/responses). This is great for pipeline workflows or research experiments.
If you plan to experiment with multiple models or very new models not yet packaged in tools like Ollama, vLLM (with HuggingFace Transformers as backend) gives you the freedom to do so by just pointing to the model path.
When to Use Ollama
Ollama is the go-to choice when ease of use and quick setup are top priorities. If you want to get a model running right now, with minimal fuss, Ollama is a good option.
For example, if you’re a developer or enthusiast who just wants to chat with an LLM locally or integrate it into a personal project, Ollama lets you do that with a few terminal commands (as we’ll see below). It abstracts away all the Python environment setup, GPU device configurations, and model conversion issues.
This makes Ollama friendly to a broader audience, including those who may not be ML experts but want to experiment with an advanced model. Even for experts, sometimes you just need a quick local endpoint for a model without digging into library internals, and Ollama provides that.
Another scenario for Ollama is when you are running on more limited hardware or want to conserve resources. Ollama’s ability to fetch quantized models means you can run larger models than you otherwise could on a given machine. If you don’t have a strong GPU, or any GPU, Ollama can still run the model on CPU (with a performance hit, of course). In fact, the developers note that while GPU is recommended (NVIDIA or AMD for acceleration), many people do run models on CPU for smaller workloads. So if you’re, say, on a laptop with 16GB RAM and no CUDA, you could still load Qwen3-1.7B or 4B in Ollama and get it to work. vLLM in CPU-only mode is possible too, but it’s less optimized for that scenario compared to the lightweight runtimes that Ollama likely employs under the hood.
Ollama is also a great fit when you want a persistent local AI service that you can use from various interfaces. Because it runs a background server by default, you can connect GUI front-ends to it or use it in your own tools via its REST API.
Interestingly, if you want to use GGUF models, vLLM is still not able to run them efficiently. I recommend using Ollama or llama.cpp for GGUF.
How to Use vLLM
Now that we’ve covered the concepts, let’s get hands-on. In this section, we’ll walk through setting up vLLM on a Linux system and demonstrate both offline (batched) inference and online serving for Qwen3 models.
Set Up vLLM
1. Install vLLM. vLLM is available as a Python package on PyPI. You can install it via pip. It’s recommended to do this in a virtual environment (conda or python3 -m venv) to avoid dependency conflicts (vLLM might install a new PyTorch).
pip install vllmThis command will fetch the latest stable vLLM (e.g., 0.9.x as of June 2025). Under the hood, vLLM will also bring in PyTorch and other dependencies. On a Linux machine with NVIDIA GPUs, vLLM should detect your CUDA and install the appropriate build of PyTorch. If you run into issues (for instance, a wrong CUDA version), you might need to manually install torch first or specify a --torch-backend as noted in the vLLM docs. But in most cases, pip install vllm “just works”.
2. Verify installation. After installation, you should be able to run the vllm command-line tool or import the library in Python. For example, try:
vllm --helpIf this prints out usage information (options for the vLLM CLI), then you have vLLM installed successfully. You can also check the version:
python -c "import vllm; print(vllm.__version__)"Running Online and Offline Inference with vLLM
vLLM can be used in two primary modes: