LM Studio vs Ollama: Which Is Better for Beginners?

You want to run an AI model on your own computer — no subscription, no cloud, no data leaving your machine. Both LM Studio and Ollama do exactly that, and both are completely free. The difference isn't performance. It's whether you've ever typed a command in a terminal.

That single question determines which one you should download first.

What Are LM Studio and Ollama?

The short version: same goal, different experience

LM Studio is a desktop app. You download it, click through a GUI, browse models like you're shopping in an app store, and start chatting in a window that looks a lot like ChatGPT. No terminal required, ever.

Ollama is a command-line tool. You install it, type ollama run llama3, and you have a local AI. It's minimal by design — if you want a chat UI on top of it, you add one separately.

Both are free, both are local, both use llama.cpp under the hood

Neither tool costs money. Neither sends your prompts to a server. Both run the same underlying model formats (GGUF), which means they can both load Llama 3, Mistral, Phi-4, Gemma, DeepSeek, and most other popular open-source models.

The compute happens on your hardware. That means your CPU, your GPU, your RAM. If you want to understand why that matters versus a cloud tool, the best free AI coding tools guide has the full picture.

LM Studio: Setup Walkthrough

Download and install (Windows, Mac, Linux)

Go to lmstudio.ai, download the installer for your OS, and run it. It installs like any other app — no terminal, no package manager.

Windows and Mac have full feature parity. The Linux build (x64 and ARM64) is production-ready as of LM Studio 0.4.x, though AMD GPU support via ROCm is still maturing — check the release notes if you're on an AMD Linux system.

Finding and downloading your first model

Once LM Studio opens, click the search icon in the left sidebar. You'll see a model browser — type "llama" or "mistral" and it shows available models with size, quantization level, and a rough estimate of RAM needed.

For a first model, look for a 7B parameter model at Q4 quantization. Something labeled llama-3-8b-instruct.Q4_K_M.gguf or similar. Click Download. LM Studio handles the rest.

You need at least 8GB of RAM to run a 7B model at Q4 quantization without the system crawling. 16GB gives you headroom.

Running your first chat

After the model downloads, click the chat bubble icon in the sidebar. Select your model from the dropdown at the top, type a message, hit enter. That's it — you're running local AI.

The experience is intentionally close to ChatGPT. If you've used ChatGPT as a coding assistant, this will feel familiar.

The local server: what it is and when you'd use it

LM Studio has a built-in local server that exposes an OpenAI-compatible API. Click the server icon in the sidebar, select your loaded model, and click Start Server.

The server runs on port 1234 by default. Any tool that accepts a custom OpenAI base URL — like Continue or Cline — can point at http://localhost:1234/v1 and use your local model instead of a cloud API. More on that in the coding tools section below.

Ollama: Setup Walkthrough

Install via one command (or installer on Windows)

On Mac or Linux, open a terminal and run:

curl -fsSL https://ollama.com/install.sh | sh

On Windows, download the installer from ollama.com and run it. After install, Ollama runs as a background service automatically — you don't need to keep a window open.

Pulling your first model

ollama pull llama3

This downloads the model. Ollama manages its own model library at ollama.com/library — you browse there to find model names, then pull by name. Popular ones include llama3, mistral, phi4, gemma3, and deepseek-coder.

Running your first chat

ollama run llama3

That opens an interactive chat session right in your terminal. Type your message, get a response. Type /bye to exit.

If you want a browser-based chat UI instead of the terminal, Open WebUI is the most popular option — it connects to Ollama's local API and gives you a ChatGPT-style interface.

The API: port 11434 and OpenAI-compatible endpoints

Ollama automatically runs an HTTP server in the background.

The default port is 11434. The API is OpenAI-compatible, so you can hit http://localhost:11434/v1/chat/completions from any tool that supports a custom base URL.

Side-by-Side Comparison

Ease of setup

| | LM Studio | Ollama | |---|---|---| | Terminal required | Never | Yes (Mac/Linux) / No (Windows) | | Install type | Desktop app | CLI / background service | | First model | In-app browser, one click | ollama pull <name> | | First chat | GUI, like ChatGPT | Terminal or add a UI | | Chat UI included | Yes | No (terminal only, or add Open WebUI) |

Model discovery and selection

LM Studio's in-app model browser is the cleaner experience. You can filter by RAM requirements and see community ratings. If you don't know what GGUF quantization means, LM Studio picks a sensible default.

Ollama's library at ollama.com is a curated list of officially supported models — fewer choices, but less room to accidentally download something your hardware can't run. Ollama handles the quantization selection for you based on your system.

Supported models

Both tools support the major open-source families:

• Llama 3 / 3.1 / 3.2 — Meta's flagship open models
• Mistral / Mixtral — strong coding and reasoning
• Phi-4 — Microsoft's small but capable model
• Gemma 3 — Google's open model
• DeepSeek Coder — specialized for code generation

LM Studio can load any GGUF file, including ones you download manually from Hugging Face. Ollama is limited to its official library, which is curated but covers all the models above.

Hardware requirements

• 8GB RAM minimum — runs 7B models at Q4 quantization (usable, but tight)
• 16GB RAM — comfortable for 7B models, can push to 13B
• 32GB RAM — opens up 30B+ models
• GPU optional — both tools use CPU if no compatible GPU is found; a GPU speeds things up significantly but isn't required to start

If your machine doesn't have enough RAM to run a useful model, a cloud alternative like Claude.ai has a free tier that requires no hardware at all.

Privacy

Both tools are completely offline. No prompts leave your machine, no account required, no usage telemetry tied to your queries. This is the main reason people choose local AI over cloud APIs — especially for sensitive code or documents.

Use with coding tools

This is where Ollama has a practical edge for developers. Because it runs as a persistent background service, coding tools can always reach it without you manually starting a server first.

• Cline supports Ollama as a custom model provider — set the base URL to http://localhost:11434/v1 and pick your model
• Continue supports both Ollama and LM Studio's local server — both configurations are documented in Continue's setup guide
• Aider works with local Ollama endpoints via the --openai-api-base flag

LM Studio works just as well for this, but you have to remember to start the local server first each session. Ollama is always on.

Which One Should You Download First?

Decision tree by user type

Start with LM Studio if:

• You've never used a terminal and don't plan to
• You want something that feels like a product, not a tool
• You want to browse and try multiple models easily
• You're coming from vibe coding with ChatGPT and want the same feeling, locally

Start with Ollama if:

• You're comfortable in a terminal
• You want something that runs quietly in the background
• You're planning to connect it to a coding tool like Cline or Continue right away
• You prefer minimal installs and don't need a built-in chat UI

Not sure? If you've opened a terminal even once and typed a command that worked, Ollama is fine. If that sentence made you nervous, start with LM Studio.

Can you use both?

Yes, and they don't interfere with each other.

LM Studio's server runs on port 1234. Ollama runs on port 11434. You can have both installed and running simultaneously — they use different ports with zero conflict. Some people use LM Studio for experimenting with new models and Ollama for feeding their coding tools.

The Bottom Line

LM Studio and Ollama are the two best ways to run local AI models for free. They use the same models, offer the same privacy, and cost the same (nothing). The only real difference is the interface.

If you want to click through a GUI and never see a terminal, download LM Studio. If you want a background service you connect tools to, install Ollama. If you're not sure, LM Studio is the lower-risk starting point — you can always add Ollama later.

The only spend involved is hardware. Both tools are free forever.