Can the RTX 4090 Laptop 16 GB run Wan 2.2 TI2V 5B?
Yes, but only just: 16-bit leaves 2.2 GB free on the RTX 4090 Laptop 16 GB.
Take 16-bit (10.0 GB). In use it comes to about 13.8 GB, with 2.2 GB left over on 16 GB.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.2_ti2v_5B_fp16.safetensors 16-bit · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/diffusion_models | 10.0 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 | wan2.2_vae.safetensors Wan 2.2 VAE · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/vae | 1.4 GB | Download → |
| Total download · keep about the same free on disk | 18.1 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: text to video workflow (.json). Drag the file into ComfyUI. All workflows →
02Every Wan 2.2 5B file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit ← SAFETENSORS · Comfy-Org | 10.0 GB | 13.8 GB | fits · 2.2 GB spare | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 5.4 GB | 9.2 GB | fits · 6.8 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 4.2 GB | 8.0 GB | fits · 8.0 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 3.8 GB | 7.6 GB | fits · 8.4 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 3.4 GB | 7.2 GB | fits · 8.8 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 2.5 GB | 6.3 GB | fits · 9.7 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 1.9 GB | 5.7 GB | fits · 10.3 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 3 GB of working memory for Wan 2.2 5B 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 5B on a RTX 4090 Laptop 16 GB, send your time per image and peak VRAM and it will appear here, credited.
04About 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 →
05Questions
How much VRAM does Wan 2.2 5B need?
Around 5.7 GB with the smallest file (Q2_K) and 9.2 GB with an 8-bit file (Q8_0). The full 16-bit file needs about 13.8 GB.
Which Wan 2.2 5B file should I download for the RTX 4090 Laptop 16 GB?
16-bit (10.0 GB) from Comfy-Org/Wan_2.2_ComfyUI_Repackaged.
How much do I need to download for Wan 2.2 5B on the RTX 4090 Laptop 16 GB?
18.1 GB: wan2.2_ti2v_5B_fp16.safetensors, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan2.2_vae.safetensors.