To run Qwen locally, install Ollama, choose a Qwen model tag, then start it with ollama run <model-tag>. The first run downloads the model. Check ollama ps while it is loaded to see whether Ollama is placing it on the GPU, CPU, or both.
How do I run Qwen locally with Ollama?
- Install Ollama using the official download page. It provides a macOS/Linux command,
curl -fsSL https://ollama.com/install.sh | sh, and a Windows PowerShell command,irm https://ollama.com/install.ps1 | iex, as well as manual installers. Check the page for the current installer instructions for your operating system. - Choose a Qwen tag from the Qwen3 or Qwen3.5 model listing. The tag determines which variant Ollama runs.
- Open a terminal and run the chosen tag, for example
ollama run qwen3,ollama run qwen3:8b, orollama run qwen3.5. The first run downloads that model if it is not already installed. - Enter a prompt in the interactive session. To leave it, use
/bye.
Ollama also exposes a local API at http://localhost:11434/api/chat; the official model listings include Python and JavaScript client examples. This is useful when you want an application to send prompts to the model instead of typing them into the terminal.
Which Qwen model can my computer run?
There is no universal model-to-RAM or model-to-GPU rule in Ollama’s listings. Choose based on the task, the model’s listed download size, the memory available on your computer, the context length you need, and the speed you can accept. Download size is not a guaranteed RAM or VRAM requirement: runtime memory use also changes with context and concurrent requests.
| Family and tag examples | Capability shown in listing | Example listed download sizes | How to use the comparison |
|---|---|---|---|
Qwen3: qwen3, qwen3:4b, qwen3:8b, qwen3:14b, qwen3:30b |
Text models, with dense and mixture-of-experts variants in the library | qwen3:0.6b: 523 MB; qwen3:14b: 9.3 GB; qwen3:30b: 19 GB; qwen3:235b: 142 GB |
Use the exact tag’s listing to compare its size and context information before downloading. |
| Qwen3.5: variants from 0.8B through 122B | Text-and-image options are listed | qwen3.5:0.8b: 1.2–1.3 GB; qwen3.5:9b: 6.6–7.6 GB; qwen3.5:122b: 81 GB |
Consult the listing for the particular tag and its capabilities; a larger listed download is not a direct memory estimate. |
These are download sizes shown in Ollama’s live model listings, not required RAM or VRAM. The listings also show context information for tags, but the context advertised for a model is not necessarily the context Ollama uses by default. There are no hardware-specific benchmarks in these listings that establish how fast a given tag will run on your computer.
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Choose by task and available memory
- For text tasks, select a Qwen3 or Qwen3.5 tag whose scale and listed context suit your workload.
- For image input, use a vision-capable tag such as a Qwen3-VL model or a text-and-image Qwen3.5 variant.
- If a model is too slow or does not fit comfortably, try a smaller variant or reduce context and parallel requests. Check actual processor placement rather than assuming a download-size figure predicts performance.
How do I check whether Ollama is using my GPU?
Start or keep the model running, then open another terminal and enter ollama ps. The PROCESSOR column reports whether the loaded model is on GPU, CPU, or split between them. This is a placement check, not a speed benchmark: performance still depends on the computer and workload.
A model can run without being entirely on the GPU. If the output shows CPU-only or a CPU/GPU split, use a smaller model or a lighter workload if the current speed is unsuitable. Ollama notes that large models can be slow on a computer without a strong GPU; its documentation does not specify one GPU or memory minimum that applies to every Qwen tag.
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How do I change Qwen’s context length?
Ollama documents a default context window of 4096 tokens. A model listing may advertise a larger context, but that does not automatically make it the runtime setting. Larger context and more simultaneous requests can increase memory use.
- For an interactive session, enter
/set parameter num_ctx 4096to set the context parameter to 4096 tokens. - To start the Ollama server with a different context length, the FAQ gives
OLLAMA_CONTEXT_LENGTH=8192 ollama serve. - For API requests, set the
num_ctxoption in the request.
Choose a context length for the amount of text your task needs, rather than setting it to the largest value in a model listing by default.
Rank #3
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How do I use Qwen with images in Ollama?
Use a vision-capable model tag; a text-only tag is not the right choice for image prompts. Ollama’s Qwen3-VL listing labels its models as text-and-image and states that Qwen3-VL requires Ollama 0.12.7. Its example command is ollama run qwen3-vl:8b. The Qwen3.5 listing also includes text-and-image variants.
Before troubleshooting an image prompt, confirm that the tag you installed supports images and that your Ollama version meets the model page’s stated requirement. Follow the model listing’s instructions for supplying an image in your prompt.
Quick Recap
Best Value
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- Simplistic Design: Enjoy the latest generation of Windows 11 Home for your everyday needs. *MSI recommends Windows 11 Pro for business use.
- Cool While Gaming: In conjunction with an ARGB fan Air Cooler, the Codex R2 features four system cooling fans; three in the front and one in the rear to pull in cool air and push heat out of the PC.
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Useful official references
- Ollama downloads and installation
- Ollama Qwen3 model listing
- Ollama Qwen3.5 model listing
- Ollama Qwen3-VL model listing
- Ollama FAQ: context length and model placement
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
