Can the RTX 4060 8 GB run Wan 2.2 T2V A14B?
Not entirely. Even Q2_K is 1.6 GB over 8 GB, so ComfyUI keeps part of it in system RAM.
Nothing fits entirely, but Q2_K (5.3 GB) overflows by only about 1.6 GB. ComfyUI keeps that part in system RAM automatically: slower than a full fit, but usable.
Also try the FP8 file (14.3 GB) on this GPU. 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 RTX 4060 8 GB, start with blocks_to_swap ≈ 31 (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 →
01With more VRAM
With 20 GB you could run Wan 2.2 T2V at 8-bit or better (FP8, 14.3 GB) with no offloading.
What changes from the RTX 4060 8 GB to the RTX 5060 Ti 16 GB → · What changes from the RTX 4060 8 GB to the RTX 3090 24 GB →
02Every Wan 2.2 T2V file on 8 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 28.6 GB | 32.9 GB | 24.9 GB too big | the original weights | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 14.3 GB | 18.6 GB | 10.6 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · QuantStack | 15.4 GB | 19.7 GB | 11.7 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 12.0 GB | 16.3 GB | 8.3 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 10.8 GB | 15.1 GB | 7.1 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 9.7 GB | 14.0 GB | 6.0 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 7.2 GB | 11.5 GB | 3.5 GB too big | noticeable loss of detail | Hugging Face → |
| Q2_K ← GGUF · QuantStack | 5.3 GB | 9.6 GB | spills 1.6 GB | heavy loss; a last resort | Hugging Face → |
“Needed” adds 3.5 GB of working memory for Wan 2.2 T2V 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-T2V-A14B-HighNoise-Q2_K.gguf high-noise model (early steps) Q2_K · QuantStack/Wan2.2-T2V-A14B-GGUF | models/unet | 5.3 GB | Download → |
| Model | Wan2.2-T2V-A14B-LowNoise-Q2_K.gguf low-noise model (late steps) Q2_K · QuantStack/Wan2.2-T2V-A14B-GGUF | models/unet | 5.3 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 | 17.6 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: FP8 workflow (.json) · GGUF Q5_K_M workflow (.json). Drag the file into ComfyUI. All workflows →
04Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 T2V on a RTX 4060 8 GB, send your time per image and peak VRAM and it will appear here, credited.
05About the RTX 4060 8 GB
The RTX 4060 8 GB is an Ada Lovelace GPU with hardware FP8, so ComfyUI can compute Comfy-Org's FP8 files natively: small and fast. (Plain FP8 files use FP8 maths with the --fast fp8_matrix_mult option.)
8 GB GDDR6 · 128-bit · 272 GB/s · Ada Lovelace. Everything that runs on it →
06Questions
How much VRAM does Wan 2.2 T2V need?
Around 9.6 GB with the smallest file (Q2_K) and 18.6 GB with an 8-bit file (FP8). The full 16-bit file needs about 32.9 GB.
Which Wan 2.2 T2V file should I download for the RTX 4060 8 GB?
Q2_K (5.3 GB) from QuantStack/Wan2.2-T2V-A14B-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Wan 2.2 T2V on the RTX 4060 8 GB?
FP8 (14.3 GB): the RTX 4060 8 GB computes it natively, while Q8_0 (15.4 GB) is unpacked on the fly.
How much do I need to download for Wan 2.2 T2V on the RTX 4060 8 GB?
17.6 GB: Wan2.2-T2V-A14B-HighNoise-Q2_K.gguf, Wan2.2-T2V-A14B-LowNoise-Q2_K.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors.