DEV Community: Aravinda 加阳

How to Import LM Studio Models into Ollama (No Re-Download!) 🚀

Aravinda 加阳 — Thu, 10 Sep 2026 00:00:00 +0000

Downloaded a bunch of models in LM Studio? 📦

Now you want to try Ollama … and it wants you to download them all over again? 😩

Nope! In this guide, you’ll learn how to import LM Studio models into Ollama using the .gguf files already on your computer. It takes about 5 minutes. ⏱️

🖥️ Example: Qwen3 8B on Windows. Works with any GGUF model.

⚡ Quick Answer

Create a one-line Modelfile that points to your LM Studio .gguf file:

FROM C:/path/to/your-model.gguf

Then import and run it:

ollama create qwen3 -f .\Modelfile.txt
ollama run qwen3

Want the easy step-by-step version? Let’s go! 👇

Step 1: Find Your GGUF Model Path in LM Studio 📋

In LM Studio, go to My Models → click ⋯ next to your model → Copy absolute path.

It looks something like this:

C:/Users/YOU/.cache/lm-studio/models/lmstudio-community/Qwen3-8B-GGUF/Qwen3-8B-Q4_K_M.gguf

Step 2: Create an Ollama Modelfile 📝

Create a folder, like C:\ollama-models
Inside it, create a file called Modelfile.txt
Type FROM, a space, then paste your path:

FROM C:/Users/YOU/.cache/lm-studio/models/lmstudio-community/Qwen3-8B-GGUF/Qwen3-8B-Q4_K_M.gguf

Save it. That’s the whole file. Really! 😄

Step 3: Install Ollama on Windows ⬇️

Grab it from the official Ollama download page and install.

Then open PowerShell and type:

ollama list

Empty? Perfect. Let’s fill it up. 👇

Step 4: Import the Model into Ollama ✨

cd C:\ollama-models
ollama create qwen3 -f .\Modelfile.txt

💡 qwen3 is just a nickname. Call it anything you like!

See success? You did it! 🎉

Step 5: Run Your Model in Ollama 👋

ollama run qwen3

Type a message, hit Enter , and chat away. 💬

Not a terminal fan? Open the Ollama app and pick qwen3 from the list. 🖱️

How to Remove an Imported Model from Ollama 🗑️

ollama stop qwen3
ollama rm qwen3

Relax, your LM Studio copy stays safe. 😌

Troubleshooting 🤔

File not found? Make sure your path ends with .gguf and you ran cd to the right folder first.
ollama not recognized? Close PowerShell and open it again.

FAQ ❓

Does importing save disk space? No. Ollama makes its own copy of the model. You save download time, not space.

Does this work with any LM Studio model? Yes, as long as the model is a .gguf file.

Can I delete the model from LM Studio after importing? Yes! Ollama keeps its own copy, so it will still work. Just test it first. 😉

That’s it! Now you know how to import LM Studio models into Ollama. One model, two apps, zero re-downloads. Happy chatting! 🤖

How to Run a Free AI Coding Assistant Locally with VS Code, opencode, and LM Studio

Aravinda 加阳 — Sat, 05 Sep 2026 00:00:00 +0000

Every AI coding assistant wants your credit card. And your code.

There’s a third option, and it costs nothing: run the whole thing on your own computer. No subscription. No internet. Your code never leaves your machine.

I set this up on a fairly ordinary PC, and it works. Here’s exactly how.

What you’re building

Three pieces working together:

LM Studio — runs the AI model on your PC. This is the engine.
opencode — the coding assistant that reads and edits your files.
VS Code — where you actually write code.

LM Studio does the thinking. opencode does the work. VS Code is where you sit.

My setup: Windows, RTX 3070 Ti with 8GB VRAM, 16GB RAM. That’s a mid-range gaming PC, not a workstation. If yours is similar, you’re fine.

Step 1: Install LM Studio

Download it from lmstudio.ai and install it like any normal app.

LM Studio lets you download and run open-source AI models directly on your computer. It’s the easiest way into local AI — no command line required.

Step 2: Download the model

Open the search inside LM Studio and look for qwen3 8b.

You’ll see a lot of versions. Check these three things before you download:

Format: GGUF
Quantization: Q4_K_M
Capabilities: “tool use” must be listed

Why those matter, in plain English:

Quantization is compression. Q4_K_M shrinks the model so it fits on a smaller graphics card. You lose a little quality, but you gain the ability to actually run it.

Tool use is non-negotiable. A coding assistant needs to open your files and edit them. A model without tool use can only chat about your code — it can’t touch it. Skip this check and nothing will work later.

Got different hardware? Pick a model that fits it. Bigger models are smarter but hungrier. An 8B model is a comfortable fit for 8GB of VRAM.

Once the download finishes, your model shows up under My Models.

Step 3: Load the model

Go to the Developer tab and select your model.

Turn on “Manually choose model load parameters” , then click the small arrow next to the model name to open the settings.

Now find context size and set it to 16000.

Context size is how much text the model can hold in its head at once — your question plus its answer plus whatever code it’s looking at. Bigger context means it understands more of your project. It also eats more VRAM.

16000 is a good number for 8GB. If you have less, go lower. If the model refuses to load, lower it again and try once more.

Step 4: Turn on the server

Flip the server status to Running.

Your model is now live at http://127.0.0.1:1234.

That address is your own machine talking to itself. Nothing is going out to the internet — which is the entire point.

Step 5: Install VS Code and opencode

Install VS Code if you don’t have it.

Then install opencode. On Windows in command prompt, the simplest route is npm:

npm install -g opencode-ai

Open a terminal inside VS Code ( Terminal → New Terminal ) and type:

opencode

opencode starts up right there in the terminal panel.

Step 6: Point opencode at your local model

Here’s the part that trips people up. opencode has no idea your model exists yet. You have to tell it, using a config file.

Create the config file — here’s mine

save it to:

C:\Users\YOUR_USERNAME\.config\opencode

Swap YOUR_USERNAME for your actual Windows username. If that folder doesn’t exist, create it.

Step 7: Pick your model and test it

Restart VS Code, then start opencode again.

Type /models and select qwen/qwen3-8b from the list.

Now ask it to do something real. Give it a file to fix.

Want proof it’s actually running locally? Switch over to LM Studio and check the logs. You’ll see tokens streaming as the assistant types.

That’s your own GPU doing the work.

What to expect

Let’s be straight about this: a local 8B model is not going to match Claude or GPT on a hard architectural problem. It’s smaller, and smaller means less capable.

But for the everyday stuff — writing boilerplate, explaining unfamiliar code, catching bugs, renaming things across files — it holds its own. And it’s fast, because there’s no network round trip.

The best part is what it costs: nothing, forever. No token limits. No monthly bill. Works on a plane.

If you have more VRAM than I do, try a larger model. Same steps, better results.