Can the RTX 4090 Laptop 16 GB run Wan 2.2 S2V 14B?
Not at full quality. The 16-bit and 8-bit files are too big for the RTX 4090 Laptop 16 GB; the largest that fits is Q2_K (9.5 GB).
Q2_K (9.5 GB) fits with only 1.7 GB to spare (about 14.3 GB of 16 GB). Quality: heavy loss; a last resort. For better quality, Q3_K_M (11.4 GB) also runs, with about 0.2 GB spilling into system RAM - a little slower, still practical.
Also try the FP8 file (16.4 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 →
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.2-S2V-14B-Q2_K.gguf Q2_K · QuantStack/Wan2.2-S2V-14B-GGUF | models/unet | 9.5 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 → |
| Also needed | wav2vec2_large_english_fp16.safetensors wav2vec2 large English FP16 (audio encoder) · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/audio_encoders | 0.6 GB | Download → |
| Total download · keep about the same free on disk | 17.1 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
02Every Wan 2.2 S2V file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 32.6 GB | 37.4 GB | 21.4 GB too big | the original weights | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 16.4 GB | 21.2 GB | 5.2 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · QuantStack | 19.6 GB | 24.4 GB | 8.4 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 16.2 GB | 21.0 GB | 5.0 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 15.0 GB | 19.8 GB | 3.8 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 13.9 GB | 18.7 GB | 2.7 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 11.4 GB | 16.2 GB | spills 0.2 GB | noticeable loss of detail | Hugging Face → |
| Q2_K ← GGUF · QuantStack | 9.5 GB | 14.3 GB | fits · 1.7 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 4 GB of working memory for Wan 2.2 S2V and a 0.8 GB reserve. Sizes 2026-10-01.
03Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 S2V on a RTX 4090 Laptop 16 GB, send your time per image and peak VRAM and it will appear here, credited.
04With more VRAM
With 24 GB you could run Wan 2.2 S2V at 8-bit or better (FP8, 16.4 GB) with no offloading: for example on the RTX 4090 24 GB, RTX 3090 24 GB, RX 7900 XTX 24 GB.
05About the RTX 4090 Laptop 16 GB
The RTX 4090 Laptop 16 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.) As a laptop GPU it runs at a lower power limit than desktop cards (80–150 W depending on the laptop). The memory verdicts are the same; speed depends heavily on how much power the laptop maker allows.
16 GB GDDR6 · 256-bit · Ada Lovelace · 80–150 W. Everything that runs on it →
06Questions
How much VRAM does Wan 2.2 S2V need?
Around 14.3 GB with the smallest file (Q2_K) and 21.2 GB with an 8-bit file (FP8). The full 16-bit file needs about 37.4 GB.
Which Wan 2.2 S2V file should I download for the RTX 4090 Laptop 16 GB?
Q2_K (9.5 GB) from QuantStack/Wan2.2-S2V-14B-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Wan 2.2 S2V on the RTX 4090 Laptop 16 GB?
FP8 (16.4 GB): the RTX 4090 Laptop 16 GB computes it natively, while Q8_0 (19.6 GB) is unpacked on the fly.
How much do I need to download for Wan 2.2 S2V on the RTX 4090 Laptop 16 GB?
17.1 GB: Wan2.2-S2V-14B-Q2_K.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors, wav2vec2_large_english_fp16.safetensors.