I finally decided to test local video AI generation after hearing about WAN 2.2 everywhere. It’s pretty amazing once you get it working right.
Lots of people with 12GB VRAM cards are having trouble running WAN 2.2 14B properly. The main issue is they’re not using GGUF format models. Regular model formats just won’t fit in our limited VRAM space.
My solution uses GGUF versions for both main model and text encoder, plus Kijay’s lightning training files and smart memory management. This gives me about 5 minute render times for short clips (4-5 seconds at 49 frames), running at around 640p resolution with just 5 total steps split as 2 high and 3 low.
Seriously, switch to GGUF if you haven’t already. The quality difference isn’t that noticeable but the speed improvement is huge.
My setup:
RTX 3060 12GB
32GB system RAM
Ryzen 3600 CPU
Required files:
WAN 2.2 High noise GGUF Q4 (8.5GB)
WAN 2.2 Low noise GGUF Q4 (8.3GB)
UMT5 XXL text encoder GGUF Q5 (4GB)
Lightning LoRA files for both high/low (600MB each)
This workflow handles both image-to-video and first/last frame generation pretty well.
Just got this working on my 4060 Ti 16GB after weeks of struggling with standard formats. GGUF conversion makes a huge difference - I was getting constant OOM errors before. Watch your batch size if you’re still having problems. Even with GGUF, some interfaces default to sizes that kill your remaining VRAM. Setting mine to 1 stopped the random crashes on longer sequences. You can swap that UMT5 text encoder for the smaller T5 variant if you’re doing simple prompts. Saves 1.5GB VRAM and prompt following was just as good for basic stuff. Only need UMT5 for complex scene descriptions. Your render times look right. I’m getting similar performance, maybe 30 seconds faster with the extra VRAM.
Thanks for the breakdown. Been running similar setups for months - GGUF’s definitely the way to go for 12GB cards.
If you’re still getting memory crashes with GGUF, try sequential CPU offloading. Saved me when I kept hitting VRAM limits on longer renders.
Saw you’re using Q4 quantization. If you’ve got extra VRAM, Q5 models give way better detail on faces and textures. Only adds 1-2GB per model.
Anyone else trying this - your system RAM speed matters. I was stuck on DDR4-2666 and offloading was garbage. Upgraded to 3200MHz and cut render times by 30%.
Lightning training works great but don’t go below 4 steps total. Breaks temporal consistency pretty bad.
had the same issue - realized my 650w psu was the problem. gpu used to throttle after a few mins while rendering. switched to 750w and now i’m gettin those steady 4-5 min render times you mentioned. also, check your temps since these gguf models really work the card hard even with lower vram.
While you’re all manually juggling VRAM and swapping models, I’ve got video AI generation running completely hands-off through Latenode workflows.
GGUF is nice, but smart automation handling the whole pipeline is the real game changer. My setup auto-checks available VRAM, picks the right quantization, manages CPU offloading, and handles batch processing for multiple clips.
I just dump prompts in a queue and walk away. The automation monitors system resources live and tweaks parameters on the fly. No more OOM crashes or endless manual adjustments.
For batches, Latenode queues everything and processes clips overnight. I wake up to finished renders without lifting a finger.
My workflow switches between Q4 and Q5 based on prompt complexity scores. Simple scenes get Q4 for speed, detailed ones automatically jump to Q5.
I’ve run this setup for months across different 12GB cards. Once you dial in the automation, zero manual work needed.