Ollama vs LM Studio vs vLLM: which one to run in 2026
These three get compared as if they were competing products. They are not: one is a desktop application, one is a model manager, one is an inference server. Picking the wrong one is rarely a performance mistake on day one and almost always becomes one on the day a second user arrives.
By Evdokiia Petrovskaia, Director, Dubir GroupSources checked
Short version: LM Studio to try models on your own machine. Ollama to wire a model into an application you are building, or to run anything on a Mac. vLLM the moment more than a handful of users or agents share one endpoint, and you are on Linux with a suitable GPU. Most teams run two of the three, and that is the right answer rather than indecision.
They are not the same kind of thing
| LM Studio | Ollama | vLLM | |
|---|---|---|---|
| What it is | Desktop app | Model manager + daemon | Inference server |
| Interface | GUI, plus a local server | CLI and HTTP API | HTTP API |
| Engine | llama.cpp, MLX | llama.cpp, MLX | Its own, PagedAttention |
| Apple Silicon | Yes | Yes | No |
| AMD | Limited | Yes | Yes, ROCm |
| Built for concurrency | No | Not really | Yes |
| Time to first token running | Minutes | Minutes | An afternoon |
The concurrency cliff
At a single request the three are close, and Ollama has been measured slightly ahead on latency. That parity is what misleads people. One user is the regime everyone tests in, and it is the only regime where the three look alike.
Add users and the curves separate. Red Hat's published comparison put vLLM near 793 tokens a second at peak throughput against Ollama's 41, with P99 latency of 80 ms against 673 ms; an independent A100 test found roughly 2.3x under eight concurrent requests. The spread between those figures tells you how much the answer depends on hardware, model and how each side was tuned. The direction never changes. vLLM's throughput scales with concurrency. Ollama's flattens almost immediately.
The mechanism is continuous batching. A conventional runtime processes a queue. vLLM keeps one batch permanently in flight, admitting new sequences and retiring finished ones without ever draining the batch. PagedAttention does the other half. It keeps KV cache in fixed pages instead of reserving each request's worst-case context up front, so the same memory holds far more simultaneous conversations.
Picking
| Situation | Run |
|---|---|
| Trying models on your laptop | LM Studio |
| Anything on Apple Silicon | Ollama or LM Studio |
| Internal tool, a few users, one GPU | Ollama |
| Production, 5-50 concurrent users, Nvidia | vLLM |
| RAG with long shared prefixes | SGLang, then vLLM |
| CPU only | Ollama or LM Studio, slowly |
Why running two is correct
All three speak an OpenAI-compatible API, so the same application code talks to a laptop and to a server. Develop against LM Studio or Ollama, where swapping a model is a click. Deploy against vLLM, where throughput is the point. What actually differs between the two environments is the model, the quantisation and the chat template. Check those match. API compatibility will not do it for you.
Before you choose a runtime
The runtime is the second decision. The first is whether the model fits your hardware at all, and that is arithmetic, not preference. See how much VRAM you need, or put your machine into the hardware calculator. And if you are still deciding whether to host anything yourself, start with when self-hosting is actually cheaper.
Questions, answered
Should I use Ollama or vLLM?
Ollama if one person at a time is using the model, or if you are on a Mac. vLLM if several people or several agents hit the same endpoint at once, and you are on Linux with an Nvidia or recent AMD GPU. At a single request they perform similarly, and Ollama is far quicker to set up; the gap opens only as concurrency rises.
Does vLLM run on a Mac?
No. vLLM targets Linux with CUDA or ROCm and has no Apple Silicon support. On a Mac the practical choices are Ollama or LM Studio, both of which now use MLX and have gained substantially from it. Reported decode throughput on an M5 Max roughly doubled after the MLX backend landed.
Is LM Studio just a GUI for Ollama?
No. They are separate projects that happen to share llama.cpp and MLX underneath. LM Studio is a desktop application with model discovery, a chat interface, a quantisation picker and an OpenAI-compatible local server. Ollama is a command-line model manager and daemon designed to be called by other software.
What makes vLLM faster under load?
Continuous batching and PagedAttention. Rather than processing requests one at a time, vLLM keeps the GPU fed by adding and retiring sequences mid-batch, and manages KV cache in pages so that memory is not reserved per-request at worst-case context length. The result is throughput that scales with concurrency instead of flattening at the first user.
Can I use one for development and another in production?
That is the normal arrangement, and it works because all three expose an OpenAI-compatible API. Develop against Ollama or LM Studio on a laptop, deploy against vLLM on the server, and the application code does not change. Confirm the model, the quantisation and the template match, since those are what differ in practice.
What about llama.cpp, MLX and SGLang?
llama.cpp and MLX are the engines that Ollama and LM Studio are built on. You can run them directly for maximum control and less convenience. SGLang is a serving engine in vLLM's class, and its RadixAttention prefix cache gives it an edge on RAG and other prefix-heavy workloads where many requests share a long common prefix.
Private AI, from the spec to the running box
Dubir Group deploys open models on client hardware from Paphos, Cyprus, for the cases where the data is not allowed to reach a third-party API. We size the machine, install it and keep it answering.