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How to run Qwen-Image 2.1 locally with llama.cpp and GGUF quants with Awesome Qwen Image

Pick a GGUF quant from Q2_K to Q8_0 that fits your memory budget.

  • Content
  • Advanced
  • 1 hour
  • From the README
  • Any agent

Authorization notice. Awesome Qwen Image is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA local-AI user who already runs llama.cpp and wants a quantized Qwen-Image 2.1 file.

The problemThe full-precision model is too large for a typical desktop, and it is unclear which quant to trade off.

The outcomeA GGUF quant that runs under llama.cpp at a memory level you choose.

What you need first

  • A runtime that matches the chosen entry point: Diffusers, ComfyUI, llama.cpp, or MLX on Apple Silicon
  • Enough disk space: the base text encoder alone is about 17.5 GB in BF16
  • A GPU with 8 to 12 GB VRAM for the INT4 ConvRot pack, or a Mac with Apple Silicon for MLX-4bit
  • Acceptance of the Qwen Research License that covers the base weights

Steps

  1. Open the Unsloth GGUF repo at huggingface.co/unsloth/Qwen-Image-2.1-GGUF.
  2. Choose a quant between Q2_K and Q8_0 based on your free memory.
  3. Download the chosen GGUF file.
  4. Run it with llama.cpp on your machine.
  5. Compare output quality at two quant levels to find the smallest one that still meets your needs.

Prompt for your agent

Using wildminder/awesome-qwen-image, download one GGUF quant between Q2_K and Q8_0 from unsloth/Qwen-Image-2.1-GGUF that fits <memory budget> and run it with llama.cpp on the prompt <prompt>. Report the chosen quant and output path. Verify the file loaded without errors and that the output matches the prompt.

Caveat. The README lists the GGUF spread but does not give per-quant memory figures, so you need to check fit yourself.

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