Can the RTX 4090 Laptop 16 GB run Wan 2.2 Animate 14B?
Download Q3_K_M (8.6 GB). With the model's working memory it needs about 13.9 GB, leaving 2.1 GB spare on 16 GB. Quality: noticeable loss of detail. For better quality, Q4_K_M (11.5 GB) also runs, with about 0.8 GB spilling into system RAM — a little slower, still practical.
Worth trying on this GPU: with an up-to-date ComfyUI, Dynamic VRAM (on by default for NVIDIA since March 2026) streams whatever does not fit from system RAM, and ComfyUI's own start-up message recommends native FP8/INT8 files over GGUF, saying they “will be faster even if they are larger than your memory”. So before settling for a heavily compressed GGUF, try the native FP8 file (17.3 GB). The file recommended above is the best one that fits entirely — the safe choice on older ComfyUI versions, AMD and Intel. Comfy blog: Dynamic VRAM →
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.2-Animate-14B-Q3_K_M.gguf Q3_K_M · QuantStack/Wan2.2-Animate-14B-GGUF | models/unet | 8.6 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 | clip_vision_h.safetensors CLIP Vision H · Comfy-Org/Wan_2.1_ComfyUI_repackaged | models/clip_vision | 1.3 GB | Download → |
| Total download · keep about the same free on disk | 16.9 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 16.9 GB, plus roughly 6 GB for Windows, ComfyUI and a browser: 22.9 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 Animate file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 34.5 GB | 39.8 GB | 23.8 GB too big | the original weights | Hugging Face → |
| FP8 SAFETENSORS · Kijai | 17.3 GB | 22.6 GB | 6.6 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · QuantStack | 18.7 GB | 24.0 GB | 8.0 GB too big | practically identical to the original | Hugging Face → |
| INT8 SAFETENSORS · Comfy-Org | 18.4 GB | 23.7 GB | 7.7 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 14.6 GB | 19.9 GB | 3.9 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 13.0 GB | 18.3 GB | 2.3 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 11.5 GB | 16.8 GB | spills 0.8 GB | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M ← GGUF · QuantStack | 8.6 GB | 13.9 GB | fits · 2.1 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 6.5 GB | 11.8 GB | fits · 4.2 GB spare | heavy loss; a last resort | Hugging Face → |
Sizes from the Hugging Face file listing, read 2026-09-25. “Needed” = file + 4.5 GB working memory for this model + 0.8 GB kept free for the system.
03The text encoder
UMT5-XXL + CLIP Vision H: 11.4 GB as 16-bit, 6.7 GB as FP8, 3.7 GB as GGUF Q4_K_M (plus CLIP Vision H (1.3 GB) for the input image). 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 16 GB the FP8 encoder fits on its own, so prompt encoding stays fast.
04About this GPU
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.
RTX 4090 Laptop 16 GB: 16 GB GDDR6 · 256-bit · Ada Lovelace · 80–150 W. Everything that runs on the RTX 4090 Laptop 16 GB →
05What more VRAM would change
With 24 GB you could run Wan 2.2 Animate at 8-bit or better (FP8, 17.3 GB) with no offloading: for example on the RTX 4090 24 GB, RTX 3090 24 GB, RX 7900 XTX 24 GB.
06Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 Animate on a RTX 4090 Laptop 16 GB, send your time per image and peak VRAM and it will appear here, credited.
07Questions
How much VRAM does Wan 2.2 Animate need?
Around 11.8 GB with the smallest file (Q2_K) and 22.6 GB with an 8-bit file (FP8), counting working memory and a small system reserve. The full 16-bit file needs about 39.8 GB.
Which Wan 2.2 Animate file should I download for the RTX 4090 Laptop 16 GB?
Q3_K_M (8.6 GB) from QuantStack/Wan2.2-Animate-14B-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
Is FP8 faster than GGUF on the RTX 4090 Laptop 16 GB?
It can be. This GPU has FP8 hardware, and ComfyUI computes FP8 natively for files made for it (Comfy-Org's fp8_scaled files), or for any FP8 file with the --fast fp8_matrix_mult option. GGUF files are unpacked on the fly, which costs some speed.
How much do I need to download for Wan 2.2 Animate on the RTX 4090 Laptop 16 GB?
About 16.9 GB for the model file, text encoder and VAE listed on this page, and the same again free on disk. With a 16 GB card, 32 GB of system RAM or more is recommended.