Imagine running one of the most advanced AI video models — WAN 2.2 — on your old GPU with just 8GB of VRAM, and still getting cinematic-quality results in minutes instead of hours. Sounds impossible? It isn’t.
In this post, we’ll walk through how you can run WAN 2.2 Rapid AIO (All-in-One) efficiently on low VRAM GPUs using ComfyUI, without compromising on visual quality. Whether you’re on an RTX 3060 or even lower, this setup will surprise you.
Why WAN 2.2 Is a Challenge for Most GPUs
WAN 2.2 (also known as Wan2.2 14B) is one of the most advanced text-to-video AI models available — capable of generating realistic motion, lighting, and emotion in AI videos.
But it comes with a major drawback: it’s extremely resource-hungry.
On a standard setup, generating a 5-second clip can take 30 to 45 minutes, and sometimes even more. The wait time alone can kill your creative flow.
That’s where WAN 2.2 Rapid AIO changes everything.
Introducing WAN 2.2 Rapid AIO – The Game Changer
The Rapid AIO (All-in-One) variant of WAN 2.2 uses FP8 precision, which drastically reduces VRAM consumption while maintaining excellent visual quality.
✅ No separate VAE or CLIP models required
✅ Single .safetensors checkpoint handles everything
✅ Works seamlessly inside ComfyUI
✅ Compatible even with GPUs as low as 8GB VRAM
I personally tested it on an RTX 3060 (12GB VRAM) — and the results were mind-blowing:
- 5-second clip rendered in just 5 minutes
- Cinematic camera movement, lighting, and emotion
- Consistent quality comparable to high-end setups
Setting It Up in ComfyUI
Getting started is super simple.
- Download the Rapid AIO model from Hugging Face – available in multiple versions: Base, V2, V3, V8 and beyond.
- Drop the model file into your ComfyUI’s
checkpointsfolder. - Load it using a Load Checkpoint Node – no extra nodes or configurations needed.
For best results, use:
- Steps: 4
- CFG: 1
- Sampler: euler_a
- Scheduler: Beta
- Resolution: 832×480
- Frames: 81 (approx. 5 seconds)
- FPS: 16
That’s it — you’re ready to generate cinematic video on your low VRAM setup.
Image-to-Video Workflow
The image-to-video workflow works just as smoothly.
You’ll need an additional CLIP Vision Encoder, which you can download from the links in the video description.
Here’s what I used:
- Same render settings as text-to-video (832×480, 81 frames, CFG 1, 4 steps)
- A still image of a red Ferrari on a desert freeway
- A prompt describing camera motion, panning, and lighting
The result looked cinematic, natural, and fast — 5 minutes for full animation.
This feature alone makes Rapid AIO perfect for turning static images into dynamic cinematic clips.
Performance Summary
| Test Type | Description | Render Time | GPU Used | Result |
|---|---|---|---|---|
| Text-to-Video | Ferrari night chase | 5 min | RTX 3060 (12GB) | Smooth, cinematic |
| Lighting Test | Mixed underlighting, blue/purple tones | 5 min | RTX 3060 (12GB) | Realistic emotion & lighting |
| Emotional Scene | Restaurant candlelight kiss | 5 min | RTX 3060 (12GB) | Elegant and expressive |
| Image-to-Video | Ferrari in daylight | 5 min | RTX 3060 (12GB) | Natural motion and lighting |
Download Links & Resources
- Workflow: T2V
- Workflow: I2V
- Model/Checkpoints (ComfyUI/models/checkpoints)
- CLIP vision encoder (ComfyUI/models/clip_vision)
- Sample Prompts
👉 Watch the full tutorial on YouTube →
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Conclusion
Running WAN 2.2 Rapid AIO on a low VRAM GPU is not only possible — it’s incredibly efficient.
In just a few minutes, you can produce cinematic-quality AI videos that used to take hours.
This model is a true game changer for creators, developers, and filmmakers who want to explore AI-driven storytelling without investing in expensive hardware.
So whether you’re experimenting with text-to-video or image-to-video, this workflow opens the door to professional-level results on practically any modern GPU.