Ollama vs vLLM: Local or Production Serving

Ollama is built for ease: one command downloads and runs a model on your laptop or a single server. vLLM is built for throughput: it batches many simultaneous requests to keep a GPU busy. Use Ollama for personal use and small teams. Use vLLM when a model serves many users or agents at once. Both are open source and expose an OpenAI-style API. Checked 31 October 2026.

The short version

What each one is

Ollama

The GitHub description says it gets you up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. It is MIT licensed. You install an app, pick a model by name and chat, and it runs an HTTP server at port 11434 for other tools. Latest release we saw: v0.40.13 on 28 October 2026. More in Ollama vs LM Studio and llama.cpp vs Ollama.

vLLM

The repository describes a high-throughput and memory-efficient inference and serving engine for LLMs. It is Apache-2.0 licensed, with about 95,900 stars and a release, v0.32.0, on 18 October 2026. Its README lists what makes it fast:

• PagedAttention for efficient management of attention key and value memory.
• Continuous batching of incoming requests, chunked prefill and prefix caching.
• Quantization in many formats, including FP8, MXFP4, NVFP4, INT8, INT4, GPTQ, AWQ and GGUF.
• Speculative decoding, including n-gram, suffix and EAGLE methods.
• Tensor, pipeline, data, expert and context parallelism for distributed inference.
• An OpenAI-compatible API server, plus an Anthropic Messages API and gRPC.
• Efficient multi-LoRA support.

It supports NVIDIA, AMD and Intel GPUs and x86, ARM and PowerPC CPUs, plus hardware plugins for Google TPUs, Intel Gaudi, Huawei Ascend, Apple Silicon and others, and says it supports 200+ model architectures from Hugging Face. Install with uv pip install vllm.

The difference in a sentence: Ollama optimises the first five minutes. vLLM optimises the thousandth concurrent request.

Side-by-side facts

Throughput and why batching matters

A GPU is expensive to leave idle. When one person chats, the GPU spends most of each second waiting for the next token to be asked for. If ten people chat at once, a smart server can process their tokens together at little extra cost. That is batching.

vLLM was designed around it. Continuous batching adds new requests to a running batch as slots free up, instead of waiting for a full batch to finish. PagedAttention stores the attention cache in pieces so memory is not wasted on empty space, which lets more requests fit on a card. Prefix caching reuses the shared start of prompts, which helps when many requests begin with the same long system prompt, as agents do.

Ollama is designed for a person at a keyboard. Its FAQ covers keeping models loaded with keep_alive, and controlling context length, and the docs describe serving on your network. It can handle several requests, but the design goal is not to extract the most tokens per second from a rack of GPUs. We did not run benchmarks, and we are not quoting throughput numbers. If throughput matters to your decision, test on your hardware with your traffic shape.

One practical GPU note. Ollama's GPU docs cover choosing which cards it uses: on a machine with several NVIDIA GPUs you can limit it to some of them, and AMD setups use ROCR_VISIBLE_DEVICES for the same job. That is device selection, not a way to split one large model across cards the way vLLM's tensor parallelism does. If your model fits on one GPU, Ollama's behaviour is simple. If it does not, plan on vLLM or accept a smaller model.

Setup, side by side

Ollama

ollama run gemma3

The model name is an example. One command pulls and runs it. For a network server, set OLLAMA_HOST=0.0.0.0:11434 and put authentication in front.

vLLM

uv pip install vllm
vllm serve <huggingface-model-id>

The first line comes from the README. The serve command is the documented pattern for starting its OpenAI-compatible server. The serve docs name --max-model-len for context length, --gpu-memory-utilization (default 0.92), --api-key and --tensor-parallel-size (default 1). Expect to learn those options and quantisation. That is the price of control.

Using them with coding agents

Both give an agent an endpoint. Ollama's is http://localhost:11434/v1/, and its docs describe Anthropic compatibility as well. vLLM's README lists an OpenAI-compatible server plus an Anthropic Messages API, so a terminal agent that speaks either format can point at it.

For a team sharing one model across many agent sessions, vLLM's prefix caching and batching suit the traffic: agents send long, repeated context. For one developer on a Mac with a small local model, Ollama is the right size. See open-source agents for tools that accept a custom base URL, and best open-source LLM for coding for models worth serving.

How to know it is time to switch

Do not move to vLLM on a hunch. Watch for these signs on your Ollama box.

• Requests wait. Users see a delay before the first token that grows with the number of people online.
• The GPU is not full. Your monitoring shows spare compute and memory while people wait, which means the server is not batching well enough for your load.
• You need more than one GPU for a single model. Splitting one large model across cards is vLLM's tensor parallelism territory.
• Agents share long prompts. Many sessions start with the same big instructions, so prefix caching would save work.
• You want an SLA. A production service needs metrics, health checks and a plan for restarts, which a serving engine is built around.

If none of those apply, stay where you are. A simpler stack is easier to fix at night.

A load test you can run

You can compare the two on your own hardware in an afternoon without special tools.

1. Pick one model that both can serve in the same quantisation level, so the test is fair.
2. Write ten realistic prompts from your own work, mixing short questions and long code reviews.
3. Start the server and send the prompts one at a time. Record time to first token and total time. That is your single-user baseline.
4. Send the same ten prompts at once from ten parallel clients. Record the same two numbers for each.
5. Repeat at 25 and 50 parallel clients if your hardware allows.
6. Watch GPU memory and utilisation while it runs.
7. Repeat the whole test on the other engine.

Plot total tokens generated per second against the number of parallel clients. A server built for batching shows total throughput rising as clients are added, while a single-user server flattens early. The shape of your own curve is the evidence you need.

Moving from Ollama to vLLM

Because both speak OpenAI-style APIs, the migration is mostly infrastructure.

1. Find the model on Hugging Face. vLLM loads Hugging Face models directly, so look up the repository behind the Ollama name you use, and check its licence on the model page.
2. Match the quantisation. Ollama models are often quantised. Pick a vLLM-supported format such as FP8, AWQ or GPTQ that fits your GPU.
3. Stand up a test server on a rented GPU first. Our hosting section and the Runpod and Modal comparisons help you pick one.
4. Change the base URL in your agent or app, and set the API key option so the server requires one.
5. Run your test prompts and compare outputs. Quantisation and sampling defaults differ, so answers will not be identical.
6. Put a proxy in front for TLS and rate limits.
7. Keep Ollama on laptops for development. It costs nothing to keep both.

A last practical point: set the context length deliberately on both. An agent that sends a large repository will be truncated silently if the limit is low, and a high limit uses more GPU memory per request, which lowers how many requests you can serve at once on vLLM.

Hardware and cost

vLLM wants GPUs. A single consumer card works for small models, and larger models need more memory than one card holds, so you spread them across several with tensor parallelism. Ollama runs on a wide range of machines, including Macs and Windows PCs, and will split a model between GPU and system memory if it does not fit, as its FAQ describes with the CPU/GPU split shown by ollama ps.

For rented GPUs, Runpod's list rates on 31 October 2026 are $2.69 an hour (Community) or $3.99 (Secure) for an H100 SXM, and $1.19 to $1.79 for an A100 PCIe. Compare providers at /compare/hosting/ and see Runpod vs Vast.ai and Modal vs Runpod. A serving engine only pays for itself if the GPU stays busy, so size the card to your traffic.

Privacy and data handling

Both keep prompts on your hardware when you self-host. Ollama also has an optional cloud tier, which you can disable with OLLAMA_NO_CLOUD. vLLM has no vendor cloud, it is a library you run. In both cases, protect the network port with authentication, and log with care since prompts may contain private data. For a wider look, read private AI and cloud vs self-hosted AI.

Limits and gotchas

• Different jobs. Using vLLM for one chat user adds complexity for no gain, and using Ollama for 100 concurrent users leaves GPU capacity unused.
• Memory first. Weights plus the cache for every active request must fit in GPU memory. More concurrent users means a larger cache.
• Updates. Both ship often; pin versions in production.
• Model support lags. A brand-new architecture may land in one before the other. Check the issue tracker.
• Auth. Neither is safe on an open network without a proxy and keys.
• Linux focus. Production vLLM is a Linux and GPU story.

Who should pick which

A solo developer or hobbyist

Ollama. Install it, pull a model, point your agent at it.

A small team sharing a model on one box

Start with Ollama. Move to vLLM when requests queue up or latency climbs under load.

A company serving an internal assistant

vLLM on a few GPUs, behind an authenticating proxy, with monitoring. Run a load test before launch.

A startup selling inference

vLLM, or a managed host that runs it for you. Check how a host prices against your own GPUs.

Someone who wants both

Develop on Ollama locally, deploy on vLLM. Both speak OpenAI-style APIs, so the client code is the same and only the base URL changes.

Our take

Think of Ollama as a desktop tool and vLLM as a server product. They overlap only in the middle, where a small team serves a model from one machine, and that is where you can reasonably start with either. The signal to move to vLLM is measurable: requests queue, latency rises, and your GPU shows spare capacity that Ollama is not using.

We did not benchmark either, and we will not claim speeds from memory. The facts above come from each project's repository and docs on 31 October 2026. Your own load test, with your model and your prompts, is the only comparison that counts.

Related pages

• Ollama vs LM Studio
• llama.cpp vs Ollama
• Open vs closed models
• Hosting