Ollama vs vLLM: Which Model Server Should You Run?
Ollama vs vLLM: compare install, hardware, concurrency, tool calling and licenses from their own repos, plus a simple rule for picking one for your agents.
Pick Ollama if you want a model running on a laptop or workstation in minutes. Pick vLLM if you serve many requests at once from Linux GPU machines and care about throughput. Both are open source, both expose an API your agent can call, and you can switch later without rewriting your agent.
We read each project's own GitHub pages in October 2026 for the facts below. Speed claims are not here: measure on your own model and hardware.
What is the difference between Ollama and vLLM?
Ollama is a runtime for running models locally with a simple command line and app. vLLM, per its README, is an open-source library for fast LLM inference and serving that began in UC Berkeley's Sky Computing Lab. One is built for convenience, the other for serving load.
| Ollama | vLLM | |
|---|---|---|
| Typical user | One developer, small team | Team serving many users or agents |
| License | MIT | Apache-2.0 |
| Install | Shell script, PowerShell, installer, Docker | uv pip install vllm, then vllm download-kernels |
| Run a model | ollama run gemma4 |
vllm serve <model> |
| Default address | 127.0.0.1:11434 | localhost:8000 |
| Platforms | macOS, Windows, Linux, Docker | Linux first, plus several accelerators |
How do they differ on hardware?
Ollama lists macOS, Windows, Linux and Docker, and lists llama.cpp as a supported backend. Its 0.40.0 release, published on 25 September, made MLX the default runtime on Apple Silicon, which matters if your team works on Macs.
vLLM's quickstart requires Linux and Python 3.11 to 3.14. The README lists NVIDIA, AMD and Intel GPUs and several CPU types, with plugins for others such as Google TPUs. Apple Silicon goes through a separate project, vLLM-Metal. If you mainly have Macs, Ollama is the easier path.
Which handles many users better?
This is vLLM's reason to exist. Its README lists PagedAttention for memory management, continuous batching, chunked prefill and prefix caching, and several forms of parallelism across GPUs.
Ollama does handle concurrency, with limits you can see. Its FAQ says each model processes one request at a time by default (OLLAMA_NUM_PARALLEL, default 1), models stay in memory for 5 minutes, and up to three models per GPU load at once. Extra requests queue. That is fine for one developer and an agent. It is a tuning job for a team.
How do they compare for agents and tool calling?
Both support tool calling, with different effort.
- Ollama: tool definitions go in a
toolsarray on/api/chat, and the docs walk through single calls, parallel calls and a full agent loop. It works out of the box for models that support it. - vLLM: you must start the server with
--enable-auto-tool-choiceand a--tool-call-parserfor your model, and some models need a chat template. The tool calling guide also warns that smaller models often get the format wrong. In return you get options liketool_choice: "required"and structured outputs.
For agents, set the context window up front. Ollama's docs recommend at least 64,000 tokens for agents and coding tools, and its default depends on your VRAM (4k under 24 GiB).
What about updates and breaking changes?
Both move fast, so pin versions.
- Ollama shipped 0.40.0 on 25 September and had reached 0.40.4 as a pre-release by 12 October. Version 0.40.2 began upgrading models downloaded by earlier versions in the background, keeps backups of the originals, and so disk use rises until you remove them.
- vLLM's 0.31.0 notes (5 October) list breaking changes, including the removal of
tokenizer_mode="slow"and the replacement ofquantization="fp8"withfp8_per_tensor. If you script vLLM flags, read the notes before every upgrade.
Which one should you pick?
- One person, a laptop, trying agents: Ollama.
- A shared GPU server for several agents or users: vLLM.
- A Mac-based team: Ollama.
- Unsure: start with Ollama, and move when queueing hurts.
Our step-by-step guide to running a local LLM server for agents shows the setup for both.
FAQ
Can vLLM run on a Mac?
Not through the main install path. vLLM's quickstart points Apple Silicon users to vLLM-Metal.
Is Ollama free for commercial use?
It is MIT licensed, per its README. Read the license of each model you download separately, since model licenses differ.
Do both offer an OpenAI-compatible API?
vLLM's README says so directly. Ollama's docs include an OpenAI compatibility page, but check which features your agent needs.
Can I run both?
Yes. Some teams prototype on Ollama and deploy on vLLM.
Browse related tools in the AI Agents & Infrastructure hub. Your next step: run the same five prompts on both and compare the answers, not the benchmarks.
