Can the RTX 2060 6 GB run Wan 2.2 S2V 14B?
Not realistically: the smallest Wan 2.2 S2V file still needs about 14 GB.
Even the smallest file (Q2_K, 9.5 GB) needs about 14.3 GB, far beyond 6 GB. It may start with heavy offloading, but this is not a realistic everyday setup. Pick a smaller model or a GPU with more memory.
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 →
01With 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.
What changes from the RTX 2060 6 GB to the RTX 3060 12 GB → · What changes from the RTX 2060 6 GB to the RTX 5060 Ti 16 GB →
02Every Wan 2.2 S2V file on 6 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 32.6 GB | 37.4 GB | 31.4 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 19.6 GB | 24.4 GB | 18.4 GB too big | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 16.4 GB | 21.2 GB | 15.2 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 16.2 GB | 21.0 GB | 15.0 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 15.0 GB | 19.8 GB | 13.8 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 13.9 GB | 18.7 GB | 12.7 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 11.4 GB | 16.2 GB | 10.2 GB too big | noticeable loss of detail | Hugging Face → |
| Q2_K ← GGUF · QuantStack | 9.5 GB | 14.3 GB | 8.3 GB too big | 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.
03What 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.
04The text encoder
Take the smallest UMT5-XXL + wav2vec2 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 the wav2vec2 audio encoder (0.6 GB)). Why the encoder does not count against VRAM →
05Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 S2V on a RTX 2060 6 GB, send your time per image and peak VRAM and it will appear here, credited.
06About the RTX 2060 6 GB
The RTX 2060 6 GB (Turing) has no FP8 compute. FP8 files still load and save memory, but ComfyUI converts them back before the maths, so there is no speed gain - Q8_0 GGUF is the closer-to-original 8-bit choice. ComfyUI can compute INT8 files natively on NVIDIA, which is worth a try where a model offers one.
6 GB GDDR6 · 192-bit · 336 GB/s · Turing. Everything that runs on it →
07Questions
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 2060 6 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 2060 6 GB?
Q8_0 (19.6 GB). The RTX 2060 6 GB has no FP8 maths, so FP8 (16.4 GB) only saves memory and is a step further from the original.
How much do I need to download for Wan 2.2 S2V on the RTX 2060 6 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.