Can the RTX 3090 24 GB run Wan 2.2 S2V 14B?
Download FP8 (16.4 GB). With the model's working memory it needs about 21.2 GB, leaving 2.8 GB spare on 24 GB. Quality: practically identical to the original.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.2_s2v_14B_fp8_scaled.safetensors FP8 · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/diffusion_models | 16.4 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 | 24.0 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). The 16-bit text encoder is a little more faithful; the smaller one is chosen here because it loads faster and needs less RAM. Sizes read from Hugging Face (2026-09-25). “Download” links start the file directly; the file name links are the same files the official ComfyUI workflows use.
System RAM: 32 GB or more. ComfyUI keeps the model file, the text encoder and the VAE in system RAM and moves them to the GPU as needed. With this set of files that is about 24.0 GB, plus roughly 6 GB for Windows, ComfyUI and a browser: 30.0 GB in total. With less RAM it still runs, but Windows starts swapping to disk and loading gets very slow. calculated
02Every Wan 2.2 S2V file on 24 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 32.6 GB | 37.4 GB | 13.4 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 19.6 GB | 24.4 GB | spills 0.4 GB | practically identical to the original | Hugging Face → |
| FP8 ← SAFETENSORS · Comfy-Org | 16.4 GB | 21.2 GB | fits · 2.8 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 16.2 GB | 21.0 GB | fits · 3.0 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 15.0 GB | 19.8 GB | fits · 4.2 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 13.9 GB | 18.7 GB | fits · 5.3 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 11.4 GB | 16.2 GB | fits · 7.8 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 9.5 GB | 14.3 GB | fits · 9.7 GB spare | heavy loss; a last resort | Hugging Face → |
Sizes from the Hugging Face file listing, read 2026-09-25. “Needed” = file + 4 GB working memory for this model + 0.8 GB kept free for the system.
03The text encoder
UMT5-XXL + wav2vec2: 11.4 GB as 16-bit, 6.7 GB as FP8, 3.7 GB as GGUF Q4_K_M (plus the wav2vec2 audio encoder (0.6 GB)). ComfyUI encodes the prompt first and can push the encoder out of VRAM before sampling, so it does not have to fit together with the model. On 24 GB the FP8 encoder fits on its own, so prompt encoding stays fast.
04About this GPU
The RTX 3090 24 GB (Ampere) 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.
RTX 3090 24 GB: 24 GB GDDR6X · 384-bit · 936 GB/s · Ampere. Everything that runs on the RTX 3090 24 GB →
05What more VRAM would change
Nothing to gain for this model: the RTX 3090 24 GB already runs a top-quality file entirely in VRAM. More memory would only help with bigger images, longer clips or several models at once.
What changes from the RTX 3090 24 GB to the RTX 4090 24 GB → · What changes from the RTX 3090 24 GB to the RTX 5090 32 GB →
06Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 S2V on a RTX 3090 24 GB, send your time per image and peak VRAM and it will appear here, credited.
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), counting working memory and a small system reserve. The full 16-bit file needs about 37.4 GB.
Which Wan 2.2 S2V file should I download for the RTX 3090 24 GB?
FP8 (16.4 GB) from Comfy-Org/Wan_2.2_ComfyUI_Repackaged.
Is FP8 faster than GGUF on the RTX 3090 24 GB?
No. This GPU has no FP8 compute, so ComfyUI converts FP8 weights back before the maths. FP8 only saves memory here; Q8_0 GGUF is the closer-to-original 8-bit pick.
How much do I need to download for Wan 2.2 S2V on the RTX 3090 24 GB?
About 24.0 GB for the model file, text encoder and VAE listed on this page, and the same again free on disk. With a 24 GB card, 32 GB of system RAM or more is recommended.