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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →To use Qwen for coding locally, install Qwen Code, run a compatible local inference server, and connect the two through Qwen Code’s Custom Provider using the server’s OpenAI-compatible API endpoint. Qwen’s provider guide documents setup examples for Ollama, vLLM, and LM Studio. The steps below use Ollama as a concrete example; model and hardware requirements depend on which Qwen model you choose.
What you need before you start
- Qwen Code: Install it using the official Quickstart instructions.
- A local model runner: Use a runner such as Ollama, vLLM, or LM Studio that exposes an OpenAI-compatible API. Qwen Code’s Model Providers guide says most local inference servers provide this kind of endpoint.
- A Qwen coding model: Choose a model your computer can run, considering available memory, storage, and the response speed you need. Ollama lists Qwen3-Coder 30B and 480B options; the 480B model has a substantial memory requirement.
Set up Qwen Code with a local runner
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Install Qwen Code and open your project
Follow the official Quickstart to install Qwen Code. Open a terminal in the directory of the code project you want to work on. The quickstart starts a project session by running
qwenfrom that directory.As an Amazon Associate I earn from qualifying purchases.
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Start a Qwen model in your runner
For Ollama, the model library lists these Qwen3-Coder commands:
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Use the tag that matches the model you intend to run. The commands and memory figures here are Ollama’s listings; they are not requirements for every Qwen model or every runner. See the Ollama Qwen3-Coder library for its model details.
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Choose Custom Provider in Qwen Code
In Qwen Code’s provider setup, select Custom Provider for the local server. Configure the provider with the model ID, the server’s local
baseUrl, and the environment-variable key name used for the API key. The exact model ID depends on the model and runner you selected. -
Set the local API endpoint
Use the endpoint for your runner. Qwen Code’s provider guide gives these examples:
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Local runner Example base URL Ollama http://localhost:11434/v1vLLM http://localhost:8000/v1LM Studio http://localhost:1234/v1These are local endpoint examples, not universal addresses for every installation. Your runner must be running and configured to serve the model at the address you enter. For servers that do not require authentication, Qwen’s examples show placeholder API key values; follow the provider guide’s configuration format rather than treating a placeholder as a real credential. See Qwen Code’s Model Providers guide.
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Start Qwen Code in the project and verify the connection
From the project directory, run
qwen. Ask it to inspect a file or explain a small part of the repository first. If that works, try a narrow, reviewable code change. Inspect the resulting diff and run the project’s own tests, build, or other checks before keeping the change.Rank #3
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Choose a model that fits your hardware
Ollama lists Qwen3-Coder 30B and 480B variants. Its library says the local 480B model requires at least 250 GB of memory or unified memory. That figure applies to running this specific 480B option locally; it is not a minimum for Qwen Code, Ollama, or smaller Qwen coding models. Check the model listing and your system’s supported and actually available memory before choosing it.
Qwen Team’s July 22, 2025 announcement describes Qwen3-Coder-480B-A35B-Instruct as a 480B-parameter model with 35B active parameters. The announcement gives a 256K native context length and says 1M tokens can be reached using extrapolation methods. These are specifications for that announced model, not a guarantee that every runner setup will expose the same context length or handle it at the same speed. See Qwen Team’s Qwen3-Coder announcement.
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If you are considering the 480B model, treat it as specialized high-memory hardware planning: a search such as “256GB RAM workstation” may help identify systems to investigate, but the Ollama listing’s 250 GB minimum and a product’s advertised RAM do not by themselves confirm compatibility. Check the full system’s supported memory and usable capacity. A smaller model may be the more practical starting point if your computer cannot meet the large model’s requirements.
Which local runner should you use?
Qwen Code documents configuration routes for Ollama, vLLM, and LM Studio, but the cited documentation does not establish a controlled performance ranking among them. Choose based on your operating system and existing setup, whether the runner exposes the compatible endpoint Qwen Code expects, and how straightforward it is to download and select your chosen Qwen model. Model choice also affects memory needs, storage footprint, and the response speed you can accept; the available specifications do not provide a like-for-like speed or code-quality comparison.
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- BUILT FOR AI—Apple silicon, and every major component that powers it, is designed to run demanding on-device AI workloads like LLM inference and training. And Apple Intelligence helps you write, express yourself, and get things done effortlessly with groundbreaking privacy protections at every step.
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Authentication: local versus hosted providers
A local-runner setup uses Qwen Code’s Custom Provider configuration to connect to your local endpoint. If you prefer hosted inference instead, Qwen Code’s authentication documentation lists Alibaba ModelStudio and third-party providers as options. It also states that the Qwen OAuth free tier was discontinued on April 15, 2026. Check the current Authentication guide for provider choices and requirements.
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