Daniel Han(@danielhanchen):Qwen-Image-2.1 works great in Unsloth Desktop via INT8 / FP8 and GGUFs! Pinned offloading to RAM also allows INT8 / FP8 to fit in under 6-8GB of VRAM, and is still relatively fast! We also made some dynamic GGUFs for it as well!

Daniel Han

@danielhanchen

Building @UnslothAI • Making open-source LLMs faster, better & more accessible • YC S24 • ex-NVIDIA ML

加入 April 2016

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Daniel Han@danielhanchen

2026.09.22 16:20

Qwen-Image-2.1 works great in Unsloth Desktop via INT8 / FP8 and GGUFs! Pinned offloading to RAM also allows INT8 / FP8 to fit in under 6-8GB of VRAM, and is still relatively fast! We also made some dynamic GGUFs for it as well!

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Unsloth AI@UnslothAI

2026.09.22 16:14

Qwen-Image-2.1 can now run locally on 12GB VRAM with Unsloth GGUFs! 🖼️ The 7B model performs on par with Nano Banana 2.0. For higher quality, you can also run Dynamic FP8 on just 6GB of VRAM via offloading. GGUF: Guide:

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