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Can the RTX 5090 32 GB run Wan 2.2 S2V 14B?

Yes. FP8 fits on the RTX 5090 32 GB with 10.8 GB to spare.

Video model · 16.3B32 GB GDDR7 · 512-bit · 1792 GB/s · BlackwellData 2026-10-01
Runs well
Yes, with room to spare.

FP8 (16.4 GB) needs about 21.2 GB in use, so 10.8 GB of the 32 GB stays free: room for a larger image, a LoRA or a ControlNet.

Best fileFP8
File size16.4 GB
VRAM needed~21.2 GB
System RAM32 GB+
Memory map · RTX 5090 32 GB21.2 GB / 32 GB
016 GB32 GB
Weights FP8 · 16.4 GBWorking memory · 4.0 GBReserve · 0.8 GBFree · 10.8 GB

01What to download

PartFileFolderSize
Modelwan2.2_s2v_14B_fp8_scaled.safetensors
FP8 · Comfy-Org/Wan_2.2_ComfyUI_Repackaged
models/diffusion_models16.4 GBDownload →
Text encoderumt5_xxl_fp8_e4m3fn_scaled.safetensors
UMT5-XXL FP8 (scaled) · Comfy-Org/Wan_2.1_ComfyUI_repackaged
models/text_encoders6.7 GBDownload →
VAEwan_2.1_vae.safetensors
Wan 2.1 VAE · Comfy-Org/Wan_2.2_ComfyUI_Repackaged
models/vae0.3 GBDownload →
Also neededwav2vec2_large_english_fp16.safetensors
wav2vec2 large English FP16 (audio encoder) · Comfy-Org/Wan_2.2_ComfyUI_Repackaged
models/audio_encoders0.6 GBDownload →
Total download · keep about the same free on disk24.0 GB

Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.

02Every Wan 2.2 S2V file on 32 GB

FileSizeNeededOn this cardQualityDownload
16-bit
SAFETENSORS · Comfy-Org
32.6 GB37.4 GB5.4 GB too bigthe original weightsHugging Face →
FP8 ←
SAFETENSORS · Comfy-Org
16.4 GB21.2 GBfits · 10.8 GB sparepractically identical to the originalHugging Face →
Q8_0
GGUF · QuantStack
19.6 GB24.4 GBfits · 7.6 GB sparepractically identical to the originalHugging Face →
Q6_K
GGUF · QuantStack
16.2 GB21.0 GBfits · 11.0 GB sparevery close to the originalHugging Face →
Q5_K_M
GGUF · QuantStack
15.0 GB19.8 GBfits · 12.2 GB spareclose; small differences in fine detailHugging Face →
Q4_K_M
GGUF · QuantStack
13.9 GB18.7 GBfits · 13.3 GB sparegood; some loss in fine detail and textHugging Face →
Q3_K_M
GGUF · QuantStack
11.4 GB16.2 GBfits · 15.8 GB sparenoticeable loss of detailHugging Face →
Q2_K
GGUF · QuantStack
9.5 GB14.3 GBfits · 17.7 GB spareheavy loss; a last resortHugging 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 5090 32 GB, send your time per image and peak VRAM and it will appear here, credited.

04About the RTX 5090 32 GB

The RTX 5090 32 GB is a Blackwell GPU with FP8 and FP4 hardware: ComfyUI computes Comfy-Org's FP8 files natively here, and NVFP4 files (where a model offers them) are faster still.

32 GB GDDR7 · 512-bit · 1792 GB/s · Blackwell. Everything that runs on it →

05Questions

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 5090 32 GB?

FP8 (16.4 GB) from Comfy-Org/Wan_2.2_ComfyUI_Repackaged.

FP8 or Q8_0 for Wan 2.2 S2V on the RTX 5090 32 GB?

FP8 (16.4 GB): the RTX 5090 32 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 5090 32 GB?

24.0 GB: wan2.2_s2v_14B_fp8_scaled.safetensors, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors, wav2vec2_large_english_fp16.safetensors.