vLLM vs Ollama vs LM Studio: which to use
Updated 2026-11-03
All three run open-weight language models on your own hardware and expose an OpenAI-compatible API, so tools written for OpenAI can talk to them. They are built for different jobs. Short version: LM Studio and Ollama for one person on a laptop or workstation, vLLM for serving a model to many users on GPU servers.
At a glance
| LM Studio | Ollama | vLLM | |
|---|---|---|---|
| What it is | Desktop app with a chat window and a local server | Command-line tool and background service | Python serving engine for GPU servers |
| Built for | One person, no coding needed | Developers and single-machine setups | Production serving to many users |
| Engine underneath | llama.cpp, and MLX on Apple silicon | llama.cpp-based | Its own CUDA engine (PagedAttention, continuous batching) |
| Model format | GGUF (and MLX on Mac) | GGUF, from the Ollama library or your own | Hugging Face weights, including AWQ, GPTQ and FP8 quantizations |
| Hardware | Windows, Mac, Linux; CPU, GPU, Apple silicon | Windows, Mac, Linux; CPU, GPU, Apple silicon | Linux with NVIDIA GPUs (also AMD and others) |
| Many users at once | Limited | Limited; handles a few parallel requests | Designed for it: batches dozens to hundreds of requests |
| Multi-GPU | Basic | Basic | Tensor and pipeline parallelism across GPUs and servers |
| Setup effort | Install and click | One command | Python environment or container, GPU drivers, configuration |
LM Studio
A desktop application: search for a model, download it, chat with it, and switch on a local API server. It is the easiest way for a non-developer to try open-weight models on a laptop or a Mac, and it runs well on Apple silicon through MLX. It is not designed to serve a whole company. Check its current licence terms if you plan to use it at work.
Ollama
A small service you install on a machine, then ollama run <model> downloads and starts a model. It is popular with developers for local work, prototypes and single-server setups, and many tools (chat interfaces, coding assistants, agent frameworks) support it out of the box. It can serve a few users in parallel, but it is not built for high concurrency or for splitting large models across many GPUs efficiently.
vLLM
An open-source inference engine from the research that introduced PagedAttention, which manages the KV cache in small blocks so a GPU can hold many conversations at once. With continuous batching it keeps the GPU busy across requests, which is what turns one server into a service for a whole team. It supports tensor parallelism across GPUs, quantized models, an OpenAI-compatible server and metrics for monitoring. It expects Linux and server GPUs, and it takes more work to set up and tune.
Other production engines in the same group include SGLang and NVIDIA TensorRT-LLM; the choice between them depends on the model, the GPUs and the features you need.
Which one for which stage
- Exploring models on a laptop: LM Studio or Ollama.
- A developer building a prototype: Ollama, then switch the base URL to a vLLM server later; the OpenAI-compatible API keeps the code the same.
- A pilot for one team on one GPU server: vLLM, so you measure realistic throughput and latency under load.
- Production for a company: vLLM (or a comparable engine) behind an authentication layer, with monitoring, logging and a second server for failover.
Sizing the server
Before choosing GPUs, check how much memory the model and its KV cache need at your context length and concurrency with the LLM VRAM calculator, and compare the monthly cost with an API in the LLM cost calculator. For hardware options see hardware for local LLMs.