How to Run a TopTier OpenSource Image Model Qwen-Image-2.1 on 10GB VRAM

The stock QwenImage21Turbo in BF16 needs about 20GB just to load and peaks near 27GB during texttoimage generation — a 10GB card like the RTX 3080 simply runs out of VRAM The fix in one sentence: quantize the image Transformer and quantize the text encoder too, all while keeping image quality high and making it even faster

Enter the star: tonera/Qwen-Image-2.1-Turbo-Nunchaku ** — an SVDQuant 4bit quantization of the official original**

You can download this quantized model from Hugging Face or the ModelScope community The companion text encoder is tonera/Qwen3-VL-8B-Instruct-Nunchaku Tested on a 32GB machine: the native BF16 model OOMs — there simply is not enough VRAM to run it After quantizing the Transformer 1024×1024 resolution, 8 steps, VRAM usage drops to 2926GB with denoising at 764 it/s — images feel instant, done in about a second With the text encoder also quantized, VRAM usage falls to 1873GB at a denoising speed of 773 it/s Even then, we could only run it on the RTX XX90 series — 24GB cards With CPU offload enabled, VRAM usage is just 873GB, meaning as long as you have more than 9GB of VRAM, you can fully run this model Some may say running the GGUF format works just as well Yes, true — but this model's runtime is the Nunchaku community edition tonera/vitoom-nunchaku Measured with CPU offload on, its denoising speed still hits 611 it/s; for 8step inference it still feels like 1second image generation

Data below: measured on the same model card, an RTX 5090, quantized version vs the BF16 original:

Note: the 243× is the relative speedup of the quantized version vs BF16 on the same 5090; absolute times differ on other cards Loading is only 115–118× faster — most of the speedup comes from stepbystep inference

An LPIPS of 0185 after 4bit quantization of a large image model is already an excellent score — it means fidelity is quite high Sacrificing a bit of quality for speed and usability is perfectly acceptable

Below is a comparison of quantizing only the Transformer vs quantizing both the Transformer and the text encoder

Both images perfectly follow the prompt constraint — "a cute girl is holding a sign reads: CreatePPTX.com " — and the version with the quantized text encoder even rendered a richer background: a potted plant

We also have to mention the tonera/flux-3-image-klein-pup-nunchaku model here It was originally released by Black Forest Labs for flux3klein, designed to expand a oneline prompt into a richer, more detailed image prompt But it works for QwenImage21 as well — if you are not great at writing prompts, this gem is for you

With the same "quantized image model INT4 encoder" combo, all of the following cards can run it locally; 30/40 series use INT4, 50 series use FP4

Note: use FP4quantized Transformer weights on Blackwell chips; use INT4 everywhere else The text encoder always uses INT weights

In one sentence: running QwenImage21 on a 10GB card is not about more VRAM — it is three things: model quantization encoder quantization offload scheduling