Wan 2.2 I2V A14B on 6 GB of VRAM
Not entirely. Even Q4_K_M is 8.0 GB over 6 GB, so ComfyUI keeps part of it in system RAM. The image-to-video Wan 2.2, and the most downloaded Wan 2.2 GGUF. Same two-model setup as the text-to-video version.
The smallest sensible file, Q4_K_M (9.7 GB), needs about 14.0 GB - 8.0 GB more than 6 GB GPU has. ComfyUI can still run it by streaming part of the model from system RAM. How much slower that is depends on your ComfyUI version, the file format and the PCIe link - see the note below.
Also try the FP8 file (14.3 GB) on NVIDIA GPUs. It does not fit entirely, but a current ComfyUI streams the overflow from RAM and native 8-bit files often beat a small GGUF that does fit. Dynamic VRAM →
Using Kijai's WanVideoWrapper instead of the native nodes? Its WanVideo BlockSwap node keeps part of the model in system RAM. To run the FP8 file (14.3 GB) on 6 GB, start with blocks_to_swap ≈ 37 (of 40). Each block is about 0.4 GB; raise the number if you still run out of memory, lower it for speed. estimate WanVideoWrapper →
01Other amounts of VRAM
02Every file, on 6 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 28.6 GB | 32.9 GB | 26.9 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 15.4 GB | 19.7 GB | 13.7 GB too big | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 14.3 GB | 18.6 GB | 12.6 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 12.0 GB | 16.3 GB | 10.3 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 10.8 GB | 15.1 GB | 9.1 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M ← GGUF · QuantStack | 9.7 GB | 14.0 GB | 8.0 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 7.2 GB | 11.5 GB | 5.5 GB too big | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 5.3 GB | 9.6 GB | 3.6 GB too big | heavy loss; a last resort | Hugging Face → |
“Needed” adds 3.5 GB of working memory for Wan 2.2 I2V and a 0.8 GB reserve. Sizes 2026-10-01. Wan 2.2 A14B uses two files of this size (high-noise and low-noise); only one sits in VRAM at a time, both must fit in system RAM.
03What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.2-I2V-A14B-HighNoise-Q4_K_M.gguf high-noise model (early steps) Q4_K_M · QuantStack/Wan2.2-I2V-A14B-GGUF | models/unet | 9.7 GB | Download → |
| Model | Wan2.2-I2V-A14B-LowNoise-Q4_K_M.gguf low-noise model (late steps) Q4_K_M · QuantStack/Wan2.2-I2V-A14B-GGUF | models/unet | 9.7 GB | Download → |
| Text encoder | umt5_xxl_fp8_e4m3fn_scaled.safetensors UMT5-XXL FP8 (scaled) · Comfy-Org/Wan_2.1_ComfyUI_repackaged | models/text_encoders | 6.7 GB | Download → |
| VAE | wan_2.1_vae.safetensors Wan 2.1 VAE · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 26.3 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
System RAM: 48 GB or more. These files (both noise models) take about 26.3 GB of RAM while loaded, 32.3 GB with Windows and ComfyUI. What happens with less. calculated
04The text encoder
Take the smallest UMT5-XXL + CLIP Vision H file (3.7 GB): it is the only one that fits in 6 GB on its own. Sizes: 16-bit 11.4 GB, FP8 6.7 GB, GGUF Q4_K_M 3.7 GB (plus CLIP Vision H (1.3 GB) for the input image). Why the encoder does not count against VRAM →