Ollama vs LM Studio: which one should you actually use to run AI locally?

If you've decided to run AI on your own computer, two names come up again and again: Ollama and LM Studio. Both are free, both run the same open models, and both are excellent. Plenty of older comparisons, including the first version of this one, describe Ollama as "the command-line one". That stopped being true in July 2025, when Ollama shipped a desktop app. So here is the comparison redone against both projects' own docs, read on 4 October 2026.

What is the difference between Ollama and LM Studio?

The difference is the center of gravity, not the AI. LM Studio is a desktop app first: you search models, download them, pick settings and chat. Ollama is a local service first: it runs in the background, keeps a library of models, and any app on your machine can ask it for an answer. Both now have a chat window and an API.

LM Studio: the model workbench

LM Studio is the better place to start if you like to see what you're doing. Its docs list the core jobs: download and run models like gpt-oss, Llama and Qwen, chat in a simple interface, search and download from Hugging Face, connect MCP servers, and serve models on OpenAI-like endpoints. It runs llama.cpp GGUF models on Mac, Windows and Linux, and Apple's MLX on Apple Silicon Macs.

The part people underrate is the settings panel. LM Studio exposes many llama.cpp load options per model, including a switch (added in 0.3.23) that forces MoE expert weights onto the CPU for low-VRAM machines. That is the same idea as llama.cpp's `--cpu-moe` and `--n-cpu-moe` flags, and it's how a mixture-of-experts model on a 12GB or 16GB card becomes usable. It also has a developer side now. The lms command line tool ships with the app, and llmster runs LM Studio's core as a headless daemon on a server with no GUI.

Ollama: the model service

Ollama is the better pick when the model is a part, not the product. Running one is a single line, ollama run qwen3:8b, and the local API on port 11434 is a common default in open-source tools. Ollama's own CLI can even set up coding tools for you: ollama launch configures Claude Code, Codex, OpenCode, VS Code and Droid to use a local model.

Ollama's desktop app arrived on 30 July 2025 for macOS and Windows, so you can chat without a terminal now. Under the hood it runs llama.cpp plus its own MLX engine on Apple Silicon. Its release notes update both regularly; v0.35.1 on 29 September 2026 lists "Updated llama.cpp and the MLX engine". A pre-release, v0.40.0, makes MLX the default on Apple Silicon for every model architecture the MLX runtime supports. If you want local models inside your editor, see how to use Ollama with Claude Code.

Is Ollama or LM Studio faster?

Neither is reliably faster. On a PC both run the same llama.cpp engine on the same GGUF file, so on identical settings the speed is close. Real gaps come from defaults: how much of the model sits on the GPU, and how big the context window is. Check both before you believe any benchmark, including a Reddit one.

There is no independent, dated benchmark from either project comparing the two on the same hardware, so we won't print one. What we can tell you is where the differences come from, from the docs:

• Context length. Ollama sets its default context by VRAM: 4k tokens under 24 GiB, 32k at 24 to 48 GiB, 256k at 48 GiB or more. A small context is fast and light. In LM Studio, context length is a per-model load setting. Compare at the same context or the numbers mean nothing.
• GPU offload. If part of the model spills to system RAM, speed drops sharply. ollama ps shows the split in its PROCESSOR column (for example "100% GPU" or "48%/52% CPU/GPU"). In LM Studio, GPU offload is another per-model load setting.
• Engine on a Mac. Both can use llama.cpp or MLX on Apple Silicon. MLX vs GGUF on the same Mac is a bigger difference than Ollama vs LM Studio.
• Engine version. Both ship llama.cpp updates on their own schedule, so a fix can land in one a week before the other.

To measure on your own machine, run ollama run <model> --verbose. After each answer it prints timings, including "eval rate" in tokens per second. Then run the same test in LM Studio. Same model file, same quantization, same context, same prompt: then compare.

Ollama or LM Studio on a Mac?

Both work well on Apple Silicon, and both can run MLX models, so pick on workflow, not speed. LM Studio needs macOS 14 or newer on an M-series chip; its docs say Intel Macs are not supported. Ollama also needs macOS 14 or newer, and runs on Intel Macs too, but CPU only.

Can you use Ollama and LM Studio together?

Yes. They don't conflict: Ollama's server listens on port 11434 and LM Studio's on port 1234, so both can run at once. A common split is LM Studio for trying and tuning new models, Ollama as the always-on backend your other apps call. The only cost is disk space if you download the same model twice.

Each app keeps its own model store, so a model pulled in one doesn't appear in the other. If you have a GGUF file, you can load that one file into both:

# LM Studio: copy a GGUF into its library (without --copy, the file is moved)
lms import /path/to/model.gguf --copy

# Ollama: point a Modelfile at the same GGUF, then build it
echo "FROM /path/to/model.gguf" > Modelfile
ollama create my-model -f Modelfile

Ollama's import docs say it does not quantize GGUF models during import, so quantize first if you need a smaller file. For keeping Ollama's side tidy afterwards, see how to delete, list and update Ollama models.

Which should you pick?

Simple rule: if you want to browse and tune models, use LM Studio; if you want a model that other tools call, use Ollama. A beginner who wants a private assistant will be happy in either app, but LM Studio's built-in model search makes the first week easier. A developer wiring AI into a project will find more tools that expect Ollama. And there's no rule against both. Whichever you choose, pick a model sized to your hardware. Looking at other options too, like vLLM or raw llama.cpp? Our four-way comparison covers them, and Open WebUI vs LM Studio covers the chat-front-end question.

Ollama or LM Studio: tune or plug in. Pick the one that matches how you want to work, size a model to your card, and get going. New to this entirely? Start with the beginner's guide to running local AI, then browse the best free models.