Qwen-Image-2.1-Turbo on an 8GB Laptop: 10 Images, 768 to 2048, Real Timings

Author: Prompt Engineer 48 · Watch on YouTube

Video authored by the channel shown, not by IANIA; all rights belong to the creator. Transcript and summary generated automatically; the original-language content is preserved.

Original language: English

Original video: Qwen-Image-2.1-Turbo on an 8GB Laptop: 10 Images, 768 to 2048, Real Timings · Channel: Prompt Engineer 48 · 07:44 · Watch on YouTube

Topics: AI developer tools, AI content generation, Model inference and optimization, Local and open-source models

Summary

The video tests Qwen-Image-2.1-Turbo on a laptop with an 8 GB RTX 4060, using a 4.2 GB Q4KM denoiser, a quantized text encoder, and ComfyUI. Across 10 images at resolutions from 768×768 to 2752×1536, generation took about 42–50 seconds around 1 megapixel and 143–148 seconds near 4 megapixels. Large text was generally readable, but small UI text and requested dashboard numbers were garbled. The presenter also notes a CPU out-of-memory error on one run and warns viewers to check the model’s Qwen research license before product use.

Key points

The setup uses an 8 GB laptop GPU and the Q4KM GGUF denoiser, which is about 4.2 GB.
The full BF16 model is 14.23 GB, so it does not fit on the tested GPU.
The text encoder is the 6.3 GB W4A8 version; the VAE is about 0.7 GB.
The ComfyUI workflow uses eight steps, CFG 1, Euler sampling, and the simple scheduler.
Ten images were generated at different resolutions with seed 42, changing only width and height.
Generation took about 42–50 seconds around 1 megapixel and 143–148 seconds near 4 megapixels.
Large text, including a poster headline and mug lettering, came out legibly, while dense small UI text and requested dashboard numbers did not.
One portrait run failed with a CPU out-of-memory error on the laptop’s 15 GB of RAM, then succeeded when retried.
The presenter cautions that the model uses a Qwen research license, not Apache, and advises checking its terms before product use.

Chapters

Start	Chapter	Summary
00:01	Test overview and model capabilities	The presenter introduces the laptop test, the model’s supported resolutions and capabilities, and its research license.
01:33	Model files and setup	The video explains the quantized model, text encoder, VAE, ComfyUI GGUF node, and workflow settings.
03:35	Generating images at different resolutions	The presenter shows the first outputs and reports generation times for a range of image sizes and prompts.
05:37	Remaining images and performance results	The final generations include a dragon, fisherman portrait, and honeybee, followed by a summary of timings and the CPU memory error.
07:11	Text limitations and closing notes	The presenter contrasts successful large text with garbled small UI text, reiterates the license caution, and wraps up the test.

Quotes

came out spelled correctly on the first try. — 00:01

I’m going with Q4KM, which leaves some room on this card. — 02:04

anywhere up near 4 megapixels, it’s about 143 to 148 seconds — 06:41

large text, like a poster headline or a mug, works well, but dense small UI text at 1344 by 768 does not. — 07:11

check that before you use these images in a product. — 07:11

Concepts and entities

Qwen-Image-2.1-Turbo, Unsloth, ComfyUI, ComfyUI-GGUF, City96, GGUF, Qwen Research License, RTX 4060 Laptop GPU

Full transcript (original language)

00:01 All right, in this video we are going to run Q and image 2.1 turbo on my laptop and you can see that the poster text here, “Visit Northport Main Coast established 1894” came out spelled correctly on the first try. Now, mind you that this is running on a 4060 laptop GPU with 8 GB of memory and the model file I’m using is only 4.2 GB. That mug says morning code, that sign says ramen and I’m going to show you the setup 10 images at 10 different resolutions and where it falls apart.

00:31 Let’s go ahead and test this out. So, this is the model card on hugging face Q and / Q and image 2.1 turbo. It’s an accelerated checkpoint of Q and image 2.1 a 7 billion parameter model and the thing to notice here is the step count. It runs in eight denoising steps with CFG set to one, so there’s no second negative pass, which is a big part of why it’s quick. Now, if you look at the resolutions listed here, it supports 2048 by 2048 for square, 2752 by 1536

01:03 for 16 by 9 and 1536 by 2752 for 9 by 16. So, these are not small images. They go up to roughly 4 megapixels. The page also lists categories like typography and poster design, UI and information layout, transparent RGBA generation and editing with a reference image. I’m only testing text to image in this video. One thing I want you to see before we go ahead, the license here is the Q and research license agreement, so this one is not Apache and I’ll come back to that

01:33 near the end. Now, the full model in BF16 is 14.23 GB and that will not fit on my 8 GB card. So, what we do is go to this repo from Unsloth Q and image 2.1 turbo GGUF. If you look at the file list, you can see Q2K at 2.47 GB, then Q4KM at 4.2, Q6K at 6.27, Q80 at and F16 at 14.23. The page recommends Q4KM for most GPUs,

02:04 and it says, “If you have to go under 4 GB, take Q3KXL instead of the 2-bit and 3-bit files because those drift at eight steps.” So, I’m going with Q4KM, which leaves some room on this card. Mind you, this file is only the denoiser. You also need the text encoder and the VAE, so let’s get those. The text encoder and VAE both come from comfy.tok.org/qwenimage2.1. The VAE is Qwen image 2.1 VAE BF16,

02:34 about .7 GB. For the text encoder, the BF16 Qwen 3 VL file is 17 and 1/2 GB, and my laptop has 15 GB of RAM, so that one is out. I picked the W4A8 version, which is 6.3 GB, and that’s the one that fits on this machine. Now, to load a GGUF file in ComfyUI, you need the ComfyUI GGUF custom node by City96. What you can do is go to custom nodes, clone that repo, and install the GGUF Python package into

03:05 the same environment ComfyUI uses. And the files go in three folders. The GGUF goes in models/diffusion_models. The text encoder goes in models/text_encoders. And the VAE goes in models/vae. Then, in the graph, the loader is called UNet loader GGUF, and I’m choosing Qwen image 2.1 turbo Q4KM. The clip loader is set to type Qwen image with the W4A8 encoder. The sampler is KSampler with eight

03:35 steps, CFG one, sampler Euler, and scheduler simple, exactly what the Onslaught page says. And you can see that the empty latent node is where we change the resolution for each image. So, the first one is a 768 by 768 sticker, a cat astronaut with a white outline. It took about 42 seconds and that includes loading the model into memory the first time. Then a 1024 by 1024 retro poster took about 50 seconds and if you look at the poster, the headline and the small line underneath

04:06 are both readable. The prompt for this one asked for big bold title text visit North Port at the top and small text main coast established 1894 at the bottom flat illustration warm orange and teal. Now, let’s go through the rest. Everything here is seed 42 eight steps same workflow only the width and height change. 1344 by 768 is the dashboard mock-up 46 seconds. I’ll get to this one in a minute. The prompt was a clean mobile app dashboard mock-up on a laptop screen titled weekly report with a bar

04:37 chart three stat cards reading 42 87 and 15 modern flat UI light theme. 2752 by 1536 the white mountain cabin with a glowing window about 143 seconds. That one was a wide cinematic landscape of a misty mountain valley at dawn a small cabin with a glowing window a river reflecting pink clouds ultra detailed. 1536 by 2752 the tall Tokyo street 143 seconds and you can see that the sign says ramen and the red umbrella

05:07 is where I asked for it. The prompt there tall vertical photo of a rainy neon street in Tokyo at night a woman with a red umbrella walking reflections on wet asphalt signs reading ramen and open. 1792 by 2400 the black mug 147 seconds and it says morning code with a steam coming off it. And for the mug studio product photo of a matte black coffee mug with the words morning code printed on it steam rising on a wooden desk next to a mechanical keyboard soft window light.

05:37 2400 by 1792 the ramen bowl 147 seconds. The ramen prompt overhead photo of a bowl of ramen with a soft egg nori scallions and chili oil on a dark slate table, chopsticks resting on the bowl, steam, rich color. 2528 by 1696, the small dragon on a pile of books in a library, 148 seconds. For the dragon, a fantasy illustration of a small dragon sleeping on a pile of books inside a huge candlelit library, floating dust, painterly style, rich

06:08 detail. 1696 by 2528, the fisherman portrait, 145 seconds. This one actually failed the first time with a CPU out of memory error, not a GPU one because of that 15 GB of RAM, and it went through when I ran it again. The portrait prompt, portrait photograph of an elderly fisherman with a weathered face and white beard, yellowing coat, soft overcast light, shallow depth of field, 85 mm lens, sharp eyes. And the biggest one, 2048 by 2048, the honeybee on lavender, 144 seconds.

06:41 And the bee, close-up macro photograph of a honeybee on a purple lavender flower, water droplets, golden hour backlight, extremely sharp, creamy bokeh. So, the pattern is simple, around 1 megapixel it’s 46 to 50 seconds, and anywhere up near 4 megapixels, it’s about 143 to 148 seconds, and it didn’t matter whether it was wide or tall. Now, here’s where it’s not perfect. On the dashboard, I asked for three stat cards reading 42, 87, and 15. The title weekly report came out right, but if you zoom

07:11 in here, the small text under it is garbled, and the numbers on the cards are not the ones I asked for. So, large text, like a poster headline or a mug, works well, but dense small UI text at 1344 by 768 does not. And this is a Q4 file at eight steps, so a Q6 or Q8 may do better, but I didn’t test those on this card. One more thing, the license is Kwen research license, so check that before you use these images in a product. So, in this video, we have seen how we can run Kwen and 2.1 turbo on an 8 GB laptop

07:44 with the onslaught Q4 KMG GUF in comfy UI with 10 images from 768 up to 2048. If you’re interested, you can go ahead and try this out on your own system as well and let me know how it goes. I’ll see you in another one. Check out the next video.