Ollama vs vLLM
Both run open-weight models. Only one of them was built to hold up when ten people ask it something at the same moment.
The short answer
Use Ollama for local development and single-user tools. Switch to vLLM once real users hit the model at the same time.
Ollama's job is to make a model runnable in minutes on a machine you already own. It does that job well, and teams reasonably reach for it first because setup is nearly instant.
The mistake is carrying that choice into a product with concurrent users. Ollama was not built to batch requests together, so ten people asking at once queue up behind each other instead of sharing the GPU. vLLM was built for exactly that case.
Side by side
Compared on what changes once real traffic arrives, not on which one installs faster.
| Dimension | Ollama | vLLM |
|---|---|---|
| Setup time | One command, minutes | Requires a GPU host and configuration |
| Built for | One user at a time, locally | Many concurrent requests on shared hardware |
| Concurrent requests | Queue behind each other by default | Batched together on the same GPU pass |
| Memory handling | Simple, not tuned for many sessions at once | PagedAttention keeps memory dense under load |
| Typical host | A laptop, a Mac, or one internal server | A dedicated GPU instance sized for peak load |
| API shape | Its own REST API, plus an OpenAI-compatible mode | OpenAI-compatible server out of the box |
| Where it breaks | Response times climb once several users overlap | Idle GPU cost when traffic is low and steady |
| Sensible trigger | Local dev, prototypes, single-user desktop tools | A product with more than one user hitting the model live |
- Request A arrivesModel starts generating
- Request B arrivesOllama waits. vLLM batches it in
- GPU passvLLM runs both together; Ollama runs A, then B
- Response timeFlat under vLLM, climbing under Ollama
The gap does not show up with one tester in a demo. It shows up in the first week with real users.
Ollama
Where it wins
- Running a model takes one command, with no GPU cluster to provision first.
- It runs well on a laptop, which makes local development and prototyping fast.
- The model library and quantised formats make trying different open models painless.
- An OpenAI-compatible mode means most client code needs no rewrite to switch to it.
Where it hurts
- It has no continuous batching, so concurrent requests queue rather than share the GPU.
- Latency degrades as soon as more than one user is active at the same time.
- It was not designed for the throughput tuning production serving needs.
- Multi-GPU serving is limited compared to a system built around it from the start.
vLLM
Where it wins
- PagedAttention manages GPU memory the way an operating system manages RAM, which is what lets it batch many requests densely.
- Continuous batching keeps latency stable as concurrent users grow, instead of degrading one queue at a time.
- It ships an OpenAI-compatible server, so a client built against a managed API mostly just points at a new address.
- Tensor parallelism spreads a large model across multiple GPUs when one is not enough.
Where it hurts
- It expects a real GPU host, so there is no laptop-first path the way Ollama has one.
- Configuration has more moving parts: batch sizes, parallelism, and memory settings to get right.
- A GPU sits there costing money whether traffic is heavy or quiet, unlike a per-token bill.
- Someone has to own upgrades, monitoring, and capacity planning, the way any self-hosted service needs an owner.
How to choose
- Pick Ollama for local development, prototyping, and any tool one person uses at a time.
- Move to vLLM once more than one user can hit the model at the same moment, in staging or in production.
- Self-hosting an open-weight model behind a real product, not a demo, is a vLLM job.
- Split the two if it fits your workflow: develop on Ollama, deploy on vLLM. Keep prompts and model versions identical across the two so behaviour does not drift between them.
- Hold off entirely if you have not decided whether to self-host at all. A managed API removes this decision and is the right default for most teams starting out.
Questions teams ask before choosing a server
01Is Ollama ready for production?
For a single-user internal tool, yes. A product with concurrent users is a different question, and the answer there is no. Ollama lacks the continuous batching that keeps response times stable as traffic grows. Teams that outgrow it usually move the same open-weight model to vLLM without changing the model itself.
02Can we develop on Ollama and deploy on vLLM?
Yes, and it is a common split. Both expose an OpenAI-compatible API, so application code barely changes. Pin the exact model version and quantisation on both sides. Test against the vLLM deployment before launch, since batching can shift output timing even when the model is identical.
03Does vLLM support the same models Ollama does?
Mostly, for the mainstream open-weight families, though the exact list and the quantisation formats each supports differ and change with new releases. Check the model you actually plan to ship against both projects' current support before committing.
04What hardware does vLLM need?
At least one GPU with enough memory to hold the model plus its key-value cache, and more for larger models or higher concurrency. Tensor parallelism can split a large model across several GPUs. Peak concurrent users, not average traffic, is what should size the hardware.
05Is self-hosting either one cheaper than a managed API?
Only at steady, high, predictable volume, and only once you count the engineering time to size, monitor and upgrade the deployment. At low or spiky volume a managed API is usually cheaper once that time is priced in.
Related
- self-hosted vs managed LLM →The bigger decision this page assumes you already made.
- what inference actually is →Why serving cost and latency, not training, decide if a feature survives.
- open-weight models worth running →The models Ollama and vLLM actually serve.
- agents that act on your systems →Built on whichever serving layer the traffic actually needs.
