Ollama vs LM Studio: Running Models Locally

Pick Ollama if you live in a terminal, want an MIT-licensed runtime and plan to serve models to other tools. Pick LM Studio if you want a desktop app with a model browser, a chat window and a built-in agent. Both run models on your own machine and expose OpenAI-style APIs, so many people install both. Facts below were checked on 31 October 2026.

The short version

The rest of this page backs those rows with what each project's own docs say, and covers the places where the choice is closer than it looks.

What each one is

Ollama

Ollama is a runtime and model manager for running language models on your own computer. The GitHub description lists the models it targets as Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and others. The repository is MIT licensed. On 28 October 2026 the latest release was v0.40.13.

You install it as an app for macOS, Windows or Linux, then run a model from the terminal. Its quickstart also lets you connect desktop apps and coding agents, and sign in to use cloud models. The cloud side is new enough that you should know about it: Ollama now sells hosted models next to the local ones, which changes what "Ollama" means on a pricing page. More on that below.

LM Studio

LM Studio is a desktop application for downloading and running local models. As of our check its homepage leads with Bionic, which it calls LM Studio's agent for open models, "natively local" and built for work and code. The page says Bionic runs on the LM Studio runtime with MLX and llama.cpp under the hood, and that you can download local models from inside the app.

Beyond the app, LM Studio ships a command-line tool, lms, which is MIT licensed on GitHub, plus a TypeScript SDK (lmstudio-js), a Python SDK (lmstudio-python) and a REST API. It also has llmster, which its docs describe as the core of the desktop app packaged to run without the GUI on Linux boxes, cloud servers and GPU rigs.

Side-by-side facts

Two things stand out. LM Studio's engine statement matters, because it means LM Studio and llama.cpp share much of the same underlying speed. And the platform note matters: LM Studio's system requirements say Intel-based Macs are currently not supported, and macOS 14 or newer on Apple Silicon is required.

Installing and running a first model

Ollama

Download the app from ollama.com, or on Linux run the install script from the docs. Then:

ollama run gemma3

The model name above is an example; pick any name from Ollama's library. The first run pulls the model, then drops you into a chat. Ollama on macOS and Windows downloads updates automatically. On Linux you re-run the install script.

Ollama runs an HTTP server on port 11434 by default. The docs say you can expose it on your network by setting OLLAMA_HOST to 0.0.0.0:11434, or by putting a proxy such as Nginx in front. On macOS you set environment variables with launchctl for the app; on Windows it reads your user and system environment variables. Set them before you open the app.

LM Studio

Download the app, open it, and search for a model inside the app. You can chat right away. For scripting, the lms CLI has commands such as:

lms get          # download a model
lms ls           # list downloaded models
lms load         # load a model into memory
lms server start # start the local API server

For a machine with no screen, LM Studio's docs recommend llmster. The install is a script, curl of https://lmstudio.ai/install.sh on Mac and Linux, or irm of https://lmstudio.ai/install.ps1 on Windows. There is also a guide for running it as a startup task on Linux with systemd.

Hardware and platform support

Both tools are only as good as the hardware under them. Their docs state the following.

Ollama. It supports Nvidia GPUs with compute capability 5.0 and above and driver version 550 or newer. Cards with compute capability 5.0 through 6.2 need driver 570 or newer. The FAQ shows how to check whether a model landed fully on the GPU: ollama ps reports a split such as 48 percent CPU and 52 percent GPU when a model spills into system memory. GPU acceleration in Docker works on Linux and on Windows with WSL2, but not on Docker Desktop for macOS, because there is no GPU passthrough there.

LM Studio. On Mac it needs Apple Silicon and macOS 14 or newer, with 16 GB of RAM recommended and 8 GB workable for small models. On Windows it supports x64 (with AVX2 required) and ARM Snapdragon X Elite, with 16 GB RAM and at least 4 GB of dedicated VRAM recommended. On Linux it supports x64 and ARM64 and ships as an AppImage.

The takeaway: on Apple Silicon both work well and LM Studio's MLX backend is a stated advantage. On an Intel Mac, LM Studio is out, and you should check Ollama's macOS requirements before you count on it. On a small laptop with 8 GB of memory, either will run small models, and neither will run a large one.

Using them with coding agents

Most coding agents can talk to an OpenAI-style or Anthropic-style endpoint, which is why these two runtimes matter for agent work.

Ollama's docs describe both OpenAI compatibility and Anthropic compatibility, and its quickstart tells you how to connect coding agents. The OpenAI base URL for a local install is http://localhost:11434/v1/. Its OpenAI page notes that the same client also supports stateless Responses requests, and that the cloud API does not support stateful Responses.

LM Studio's developer docs describe the same idea in more places. They list OpenAI-compatible endpoints for chat completions, responses, embeddings and structured output, and an Anthropic-compatible Messages endpoint "against your local LM Studio server". There is also an API for using MCP servers: the docs list a page called Using MCP via API.

That gives you a route for pointing a terminal agent at a local model. Our open-source agents list covers which agents let you set a custom base URL. OpenCode and Aider are common choices for a local setup. A caution that applies to both runtimes: a small local model will be far weaker at multi-step tool use than a hosted frontier model. Test your agent on a small task before you rely on it. For help picking the model, see best open-source LLM for coding.

Cloud and pricing

Local use is free on both. The money questions are about the hosted side.

Ollama's pricing page lists four tiers. The Free plan is $0 and pays per token for cloud models, with local use unlimited. Pro is $20 a month, or $16.67 a month billed at $200 a year, and includes $60 of usage credits a month plus access to larger models. Max is $100 a month with $300 of usage credits, early access to the newest models and 10 concurrent requests. The team plan starts at $500 a month with $1,000 of shared usage credits. The free plan's concurrency is listed at 3 requests. The pricing page also lists per-token rates for cloud models, including DeepSeek, which match the lab's own listed rates, such as $0.15 input and $0.60 output for the flash model at off-peak.

LM Studio's pricing page has a Free plan that, in its own words, includes the Bionic agent and local model support. Cloud credits cover usage beyond the allowance in your plan. For teams it offers centralised billing using inference credits, with subscription plans for teams "coming soon".

If you only want local, neither costs anything. If you want a single place to reach a big open model you cannot fit on your GPU, compare these hosted prices with other routes in OpenRouter alternatives and the hosting section.

Privacy and data handling

Running locally is the privacy argument, and it holds for both: the model runs on your machine and your prompts stay there.

The cloud features are where you need to read closely.

• Ollama. The FAQ documents a way to turn cloud features off, using a setting called disable_ollama_cloud or the environment variable OLLAMA_NO_CLOUD. If you work with sensitive code, set it and confirm.
• LM Studio. The homepage says its cloud services are Zero Data Retention across the board, and that voice and audio in Bionic are processed locally and never leave the device.

A separate point on exposure: both tools can listen on your network. An open Ollama port with no authentication is a common mistake. LM Studio's docs list an Authentication page under its server settings. Whichever you pick, keep the server on localhost unless you have put access control in front of it.

Limits and gotchas

• Concurrency. Ollama's free cloud plan allows 3 concurrent requests. Local concurrency depends on your memory, since each loaded model takes RAM or VRAM. The FAQ covers keep_alive so models stay loaded or unload on a schedule.
• Context length. Ollama's docs have a context length page and an OLLAMA_CONTEXT_LENGTH variable. A model that "works" in a chat can still truncate a large repository, so set this deliberately.
• Model formats. LM Studio draws on MLX and llama.cpp formats. Ollama has its own model library and an import path for GGUF files through a Modelfile.
• Updates. Ollama ships fast. A release came out within three days of our check, so pin a version if you run it in production.
• Intel Macs. LM Studio does not support them.
• Products are changing. LM Studio now leads with an agent product. If you only wanted the model runner, the CLI and llmster are the parts to read about.

Who should pick which

A developer who already lives in the terminal

Ollama. One command pulls a model, one command serves it, and the MIT licence means you can read and fork the runtime. It fits a script, a Docker container or a CI box.

Someone who wants to explore models without commands

LM Studio. The app is built around finding, downloading and chatting with a model, and it shows what fits on your machine. Add lms later when you want automation.

A team that needs a shared model server

Either can serve a network, but neither is built as a high-throughput production server. If many people hit one box at once, look at Ollama vs vLLM, since vLLM is designed for that load.

A privacy-minded user on a laptop

Either, with cloud features off. Pick based on whether you prefer a window or a terminal.

Our take

These two overlap more than their reputations suggest. Both wrap a llama.cpp-class engine, both serve OpenAI-style and Anthropic-style APIs, and both are free for local use. The real differences are who they are built for and what they are turning into.

Ollama is a developer tool with an open licence and a clear server story. Its recent move into paid cloud models widens the product, but the local runtime stays MIT. LM Studio is a polished desktop product that is now pushing an agent called Bionic. That is good if you want an all-in-one app, and less clear if you just want a quiet model server.

We built this verdict from the docs, not from a speed test, and we did not benchmark tokens per second. If speed matters to you, run the same model and quantisation on both and time a fixed prompt. The honest advice for most readers is to install both, since they sit side by side without conflict, and keep whichever one you open more often.

Checklist before you commit

1. Check your hardware against the requirements above, especially Mac chip and Windows AVX2.
2. Decide whether you want cloud features. If not, disable them.
3. Pick a model size that fits your RAM or VRAM with room for context.
4. Start the server on localhost and test it with a one-line OpenAI client call.
5. Point your coding agent at the base URL and run a small task.
6. Write down the version you tested, since both tools update often.

Related pages

• llama.cpp vs Ollama for the engine underneath
• Ollama vs vLLM for serving many users
• Open vs closed models for the model choice
• Private AI for the wider self-hosting picture