Qwen is a family of models you install into a local runtime on Ubuntu, not a feature Ubuntu switches on. There are two documented routes. Ollama is the quickest start: install it as a snap, then run a Qwen tag such as qwen3:0.6b. llama.cpp gives more control over model files and hardware use, but requires building software and choosing a GGUF file. The official documentation does not publish reliable RAM, VRAM, or disk minimums for each Qwen model on Ubuntu 24.04 or 26.04, so the practical way to pick a size is to start small, confirm it loads, and move up only if your hardware handles it.
What you need to know before installing
- Qwen is model software. Ubuntu’s own AI guidance states that “As of Ubuntu 26.04 LTS, a fresh installation of Ubuntu Desktop contains no built-in AI tooling.” (Ubuntu Wiki, “AI on Ubuntu”). You add the runtime and the model yourself.
- Ollama is the fastest documented route. Ubuntu’s example is
sudo snap install ollamafollowed byollama run qwen3:0.6b. - llama.cpp is the controllable route. It requires build tools, a compiled binary, and a GGUF model file.
- Hardware limits are not published per model. Parameter counts and file sizes are not guaranteed minimum system requirements. Check your own free memory, GPU memory if you plan to offload, and free disk space before each download.
Path A: Install Qwen with Ollama
Ollama is the best first step if you want a working model in a few minutes and do not need to manage model files by hand.
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- Install Ollama from the snap store:
sudo snap install ollama - Run the small Qwen3 example that Ubuntu documents:
ollama run qwen3:0.6bThis confirms the runtime works. It does not show that larger models will run well on your computer.
- To use other sizes, choose a tag that Qwen’s Ollama instructions list, such as
qwen3:8borqwen3:30b-a3b:ollama run qwen3:8b
Qwen’s instructions describe the Ollama service as the piece you start with ollama serve. Keep it running while you use Ollama’s API, which defaults to http://localhost:11434/v1/. Qwen’s repository recommends Ollama v0.9.0 or higher for that documentation. Version recommendations and tag names change, so check the current Qwen Ollama page before you copy a tag.
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Qwen3 has thinking enabled by default. Inside an Ollama session you can switch it with /set nothink and back with /set think. Qwen also documents how to set num_ctx (context length) and num_predict (output length). Qwen specifically warns that Ollama’s default context may be unsuitable for Qwen3, so set context deliberately for your workload. Do not treat any single context value as safe for every model or machine: a larger context generally needs more memory.
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Path B: Build llama.cpp and run a GGUF model
Use this route if you want to choose the exact quantization, decide how much work goes to the GPU, or run the llama.cpp binaries directly.
- Install the Ubuntu build tools:
sudo apt install build-essential - Get llama.cpp from its official repository and build it with CMake:
git clone https://github.com/ggml-org/llama.cpp cd llama.cpp cmake -B build cmake --build build --config ReleaseThe compiled programs are placed under
./build/bin/. - Use a current build. Qwen’s Qwen3 repository recommends llama.cpp b5401 or later for full Qwen3 support. Qwen’s dedicated llama.cpp guide says Qwen3 and Qwen3MoE are supported from b5092. If you are installing today, follow the stricter b5401 recommendation and check for newer releases.
- Download a GGUF model file. Qwen’s guide uses an official Qwen3-8B GGUF in the Q4_K_M quantization, saved into the current directory. GGUF is a format that stores model weights and related model information. Quantization reduces the footprint of the weights, at some cost in output quality.
The guide’s 8B Q4_K_M example shows one combination that Qwen documents. It is not a promised RAM or VRAM minimum for your machine.
CPU and GPU use
llama.cpp uses the CPU by default, and Qwen’s guide says you can specify the number of CPU threads. CPU inference is documented, but the guide does not promise interactive speed for any particular model size.
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GPU offload requires a build compiled with GPU support. Qwen’s guide lists CUDA, hipBLAS, SYCL, Vulkan, and other backends. Your driver, GPU, and chosen backend all matter, and the backend-specific build instructions for your GPU are the ones to follow. Installing build-essential alone does not enable GPU acceleration.
Choosing a model size without a guaranteed minimum
Because the official sources do not publish per-model hardware thresholds, use a test-first process rather than a lookup table.
- Start with the smallest variant. Confirm that
qwen3:0.6bor an equivalent small build loads and answers before you try anything larger. - Move up one size at a time. After each step, check how much memory the model uses while it runs and whether responses are usable for your work.
- Treat quantization as a lever. A smaller quantization reduces file size and runtime footprint. Qwen’s example uses Q4_K_M, but other quantizations may suit your hardware better, and the guide does not compare them for you.
- Watch context length. Larger context settings can increase resource needs, so raise them only when your task requires it.
- Check storage first. Model files take disk space. The official guide does not state a universal capacity for a usable installation. An external SSD for local model files is an optional option if your internal drive lacks room; Qwen’s documentation does not recommend a particular drive or capacity.
Avoid rules such as “8 GB of RAM runs an 8B model.” Such claims need a model-specific, current source that names the quantization, context length, runtime, and workload. The official documentation reviewed does not supply one for either Ubuntu release.
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Ubuntu 24.04 and 26.04 differences
Ubuntu’s AI guidance explicitly addresses Ubuntu 26.04. It documents the Ollama snap example and gives sudo apt install cuda-toolkit as the package path for installing the CUDA toolkit on 26.04. That command establishes the toolkit package path only. It does not confirm that a specific GPU, driver, Qwen runtime, or model combination will work.
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The official sources reviewed do not include a full comparison of Ollama and llama.cpp behavior on Ubuntu 24.04 versus 26.04. If you are on 24.04, follow the same steps but verify each command’s output on your system, and do not assume that any GPU result carries over from 26.04.
Which path fits your needs
| Choice | Better fit | Trade-off |
|---|---|---|
| Ollama | Fastest beginner setup and a simple model-tag workflow | Less low-level control. Confirm current tags and set context deliberately. |
| llama.cpp | Control over GGUF quantization, CPU and GPU offload, and direct use of the binaries | Requires a build, a backend choice that matches your GPU, and selecting and storing model files yourself. |
This comparison reflects the official setup steps. It is not a benchmark, and neither route is shown here to be faster.
If something does not work
- Ollama API calls fail: confirm that
ollama serveis running, since Qwen’s instructions say the service must stay up. - The model runs out of memory or responds very slowly: step down to a smaller tag or quantization, and reduce context length.
- GPU is not used in llama.cpp: confirm the build was compiled with a GPU backend that matches your hardware, rather than relying on the default CPU build.
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The Bottom Line
For most readers, start with Ollama and qwen3:0.6b to confirm the setup works, then move to a larger tag only after checking your own memory and storage. Choose llama.cpp when you need to control the model file, quantization, or GPU use.
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