Ollama vs vLLM
Ollama is for running open-weight models conveniently, including on CPU; vLLM is for serving one model to many concurrent users on a GPU, as fast as the hardware allows. If you are asking which to put on a server you rent by the month, the answer is almost always Ollama — vLLM without a GPU defeats its own purpose.
At a glance
| Ollama | vLLM | |
|---|---|---|
| Primary goal | Convenience — pull a model, get an API | Throughput — saturate a GPU |
| Hardware | CPU or GPU | GPU, effectively required |
| Concurrency | Modest; fine for a team or an app | High; paged attention and continuous batching |
| Model formats | GGUF, quantized by default | Full-precision and quantized safetensors |
| Setup effort | One command per model | Launch flags, memory tuning, model config |
| API | Own API plus OpenAI-compatible | OpenAI-compatible |
| Realistic monthly cost | $16.99 CPU plan here | GPU instance, $400-1,000+ elsewhere |
They are not competing for the same job
Benchmarks that put these two side by side usually measure tokens per second and declare vLLM the winner, which is true and beside the point. vLLM exists to keep an expensive GPU busy: continuous batching means forty simultaneous requests cost barely more wall-clock than one. That engineering only pays off when there is a GPU and there are concurrent users.
Ollama optimises for the opposite constraint — the time between deciding to try a model and having an endpoint. It manages downloads, quantization and model switching, and it runs acceptably on ordinary CPU cores.
What CPU inference actually delivers
On dedicated cores, a quantized 7-8B model answers at roughly 5-15 tokens per second. That is slower than reading speed for long answers and completely fine for classification, extraction, summarising, RAG answers and automation steps. A 13B model on a 12 GB plan works for batch and background jobs.
What CPU cannot do is realtime conversation with a large model, or serving many users at once. If either is a requirement, the honest recommendation is a GPU provider — and we would rather say that than sell a plan that disappoints.
When vLLM is genuinely the right answer
You have a GPU or are willing to rent one, you are serving one model to many users, and latency is a product requirement. That is a real and common situation — it is just not the situation of someone comparing monthly hosting plans.
We list vLLM in the catalogue and do not sell it, for exactly this reason.
The verdict
Choose Ollama for a private model endpoint you can afford to leave running: CPU-friendly, trivial to operate, OpenAI-compatible. Choose vLLM when you have GPU capacity and concurrency to justify it. If you are unsure, start with Ollama on CPU — the cost of finding out is $16.99, and the API you write against is the same either way.
Mentioned on this page
Ollama
Your private LLM server — pull a model, get an API, keep every token on your own box.
from $16.99/moDetails →
vLLM
High-throughput model serving — GPU territory, listed for completeness.
on requestDetails →
llama.cpp
The reference GGUF inference server, with nothing between it and the CPU.
from $13.99/moDetails →
LocalAI
A drop-in OpenAI replacement for text, images, audio and embeddings.
from $16.99/moDetails →Questions
Can vLLM run on CPU?
Is Ollama fast enough for a chatbot?
Do both speak the OpenAI API?
Put your AI stack on your own box
Pick an app, pick a size, and have it running today. Month to month, cancel whenever.