Can the RTX 5060 Ti 16 GB run Wan 2.1 I2V 14B 480P?
Not at full quality. The 16-bit and 8-bit files are too big for the RTX 5060 Ti 16 GB; the largest that fits is Q4_K_M (11.3 GB).
Q4_K_M (11.3 GB) fits with only 0.4 GB to spare (about 15.6 GB of 16 GB). Quality: good; some loss in fine detail and text. For better quality, Q5_K_M (12.7 GB) also runs, with about 1.0 GB spilling into system RAM - a little slower, still practical.
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 →
Using Kijai's WanVideoWrapper instead of the native nodes? Its WanVideo BlockSwap node keeps part of the model in system RAM. To run the FP8 file (16.4 GB) on RTX 5060 Ti 16 GB, start with blocks_to_swap ≈ 13 (of 40). Each block is about 0.4 GB; raise the number if you still run out of memory, lower it for speed. estimate WanVideoWrapper →
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.1-i2v-14b-480p-Q4_K_M.gguf Q4_K_M · city96/Wan2.1-I2V-14B-480P-gguf | models/unet | 11.3 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_2.1_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 | 19.6 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
02Every Wan 2.1 I2V 480P file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 32.8 GB | 37.1 GB | 21.1 GB too big | the original weights | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 16.4 GB | 20.7 GB | 4.7 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · city96 | 18.1 GB | 22.4 GB | 6.4 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · city96 | 14.2 GB | 18.5 GB | 2.5 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · city96 | 12.7 GB | 17.0 GB | spills 1.0 GB | close; small differences in fine detail | Hugging Face → |
| Q4_K_M ← GGUF · city96 | 11.3 GB | 15.6 GB | fits · 0.4 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · city96 | 8.6 GB | 12.9 GB | fits · 3.1 GB spare | noticeable loss of detail | Hugging Face → |
“Needed” adds 3.5 GB of working memory for Wan 2.1 I2V 480P 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 2.1 I2V 480P on a RTX 5060 Ti 16 GB, send your time per image and peak VRAM and it will appear here, credited.
04With more VRAM
With 24 GB you could run Wan 2.1 I2V 480P 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 5060 Ti 16 GB to the RTX 3090 24 GB → · What changes from the RTX 5060 Ti 16 GB to the RTX 5070 Ti 16 GB → · What changes from the RTX 5060 Ti 16 GB to the RTX 4090 24 GB → · What changes from the RTX 5060 Ti 16 GB to the RTX 5090 32 GB →
05Questions
How much VRAM does Wan 2.1 I2V 480P need?
Around 12.9 GB with the smallest file (Q3_K_M) and 20.7 GB with an 8-bit file (FP8). The full 16-bit file needs about 37.1 GB.
Which Wan 2.1 I2V 480P file should I download for the RTX 5060 Ti 16 GB?
Q4_K_M (11.3 GB) from city96/Wan2.1-I2V-14B-480P-gguf. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Wan 2.1 I2V 480P on the RTX 5060 Ti 16 GB?
FP8 (16.4 GB): the RTX 5060 Ti 16 GB computes it natively, while Q8_0 (18.1 GB) is unpacked on the fly.
How much do I need to download for Wan 2.1 I2V 480P on the RTX 5060 Ti 16 GB?
19.6 GB: wan2.1-i2v-14b-480p-Q4_K_M.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors, clip_vision_h.safetensors.