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To use Qwen for coding locally, install Qwen Code, launch it from your project directory, and configure a Custom Provider to point to a local model runner’s OpenAI-compatible API. Qwen Code documents setup examples for Ollama, vLLM, and LM Studio. The exact model you can run depends on your hardware: Ollama lists a 250 GB memory minimum for its Qwen3-Coder 480B option, but that is not a general requirement for Qwen Code or smaller models.
Before you start: choose a runner and model
Qwen Code connects to a local inference server through its Custom Provider configuration. Qwen Code says most local inference servers, including Ollama, vLLM, and LM Studio, provide an OpenAI-compatible API endpoint. The provider setup is similar across runners, but the endpoint, model ID, and method of downloading or selecting a model differ. See the Qwen Code model-provider guide.
Ollama’s Qwen3-Coder library lists runnable 30B and 480B options. Its stated minimum of 250 GB of memory or unified memory applies to running the 480B model locally—not to Qwen Code, Ollama, or every Qwen coding model. Check the selected model’s requirements and your system’s supported and available memory before downloading it. See the Ollama Qwen3-Coder library.
Runner and model selection
- Choose a runner that works on your operating system and exposes the compatible API endpoint Qwen Code expects.
- Check how the runner downloads and selects the Qwen model you want to use.
- Choose a model that fits your available memory, storage, coding needs, and acceptable response speed. The cited documentation does not provide a controlled speed or code-quality comparison among runners, so it does not establish which one is fastest or best.
Install Qwen Code and open your project
- Install Qwen Code using the official quickstart instructions.
- Open a terminal in the project you want to work on. The quickstart says to start the CLI from the project directory by running
qwen.
Configure a Custom Provider for your local runner
In Qwen Code, select Custom Provider for the local server. Add a provider entry with the model ID, the server’s baseUrl, and the environment-variable key name used for the API key. Qwen’s examples use placeholder API-key values for servers that do not require authentication; follow the provider guide’s configuration format for your setup.
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| Local runner | Example base URL in Qwen Code’s guide | What to supply |
|---|---|---|
| Ollama | http://localhost:11434/v1 |
The model ID selected in Ollama and the API-key environment-variable key name. |
| vLLM | http://localhost:8000/v1 |
The model ID served by vLLM and the API-key environment-variable key name. |
| LM Studio | http://localhost:1234/v1 |
The model ID served by LM Studio and the API-key environment-variable key name. |
These are examples from the Qwen Code model-provider guide; use the actual endpoint and model ID configured in your runner if they differ.
Start a Qwen model and use it in your project
With Ollama, the model library lists these commands for its Qwen3-Coder options:
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ollama run qwen3-coder:30bollama run qwen3-coder:480b
Run the command for the model you chose, then start Qwen Code in your project with qwen. Ask it to inspect the repository or make a small, reviewable change. Review the resulting diff and run the project’s own checks before keeping the changes.
What to know about the 480B model
Ollama lists at least 250 GB of memory or unified memory to run its Qwen3-Coder 480B option locally. Treat that as a model-specific hardware requirement, not a baseline for a local Qwen coding setup. A high-memory workstation may be relevant if you specifically plan to run this variant; verify the system supports the required memory and that enough capacity is actually available.
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Qwen Team’s July 22, 2025 announcement describes Qwen3-Coder-480B-A35B-Instruct as a 480B-total-parameter model with 35B active parameters. It gives a 256K native context length and says 1M tokens can be reached using extrapolation methods. Those are specifications for that announced model, not a guarantee that every runner configuration supports the same context length. See Qwen Team’s announcement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Authentication and hosted alternatives
For the local-runner workflow, use Custom Provider rather than Qwen OAuth. Qwen Code’s authentication documentation says the Qwen OAuth free tier was discontinued on April 15, 2026, and lists Alibaba ModelStudio, third-party providers, and Custom Provider among its top-level choices. Readers who do not want to run inference locally can consider the hosted-provider routes documented there. See the Qwen Code authentication guide.
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- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
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