Can the RTX 5060 Ti 16 GB run Wan 2.2 T2V A14B?
Timed on my own card, not estimated: 19 minutes for a 81-frame clip with the FP8 file, peaking at 13.3 GB.
Q5_K_M (10.8 GB) fits with only 0.9 GB to spare (about 15.1 GB of 16 GB). Quality: close; small differences in fine detail. For better quality, Q6_K (12.0 GB) also runs, with about 0.3 GB spilling into system RAM - a little slower, still practical.
Also try the FP8 file (14.3 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 (14.3 GB) on RTX 5060 Ti 16 GB, start with blocks_to_swap ≈ 9 (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 →
01My measurements
| Label | Setup | Result | Peak VRAM | Date | Source |
|---|---|---|---|---|---|
| measured | wan2.2_t2v_high/low_noise_14B_fp8_scaled.safetensors · 832x480 · 20 steps 1 timed run after 1 warm-up (1149.4 s). Peak RAM 28.6 GB. ComfyUI 0.25.0 portable, PyTorch 2.12 cu130, 32 GB RAM. Text to video, 81 frames (about 5 s), 832x480, 20 steps (10 high-n | 1146.1 s / clip (81 frames) | 13.3 GB | 2026-09-27 | |
| measured | Wan2.2-T2V-A14B-HighNoise/LowNoise-Q5_K_M.gguf · 832x480 · 20 steps 2 timed runs (1535.0, 1544.5 s) after 1 warm-up (1538.7 s). Peak RAM 33.1 GB. ComfyUI 0.25.0 portable, PyTorch 2.12 cu130, 32 GB RAM. Text to video, 81 frames (about 5 s), 832x480, | 1539.8 s / clip (81 frames) | 14.0 GB | 2026-09-27 | |
| measured | Wan2.2-T2V-A14B-HighNoise/LowNoise-Q8_0.gguf · 832x480 · 20 steps 1 timed run after 1 warm-up (1516.6 s). Peak RAM 32.9 GB (warm-up), 31.9 GB (timed run). ComfyUI 0.25.0 portable, PyTorch 2.12 cu130, 32 GB RAM. Text to video, 81 frames (about 5 s | 1515.9 s / clip (81 frames) | 13.1 GB | 2026-09-27 |
02What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.2-T2V-A14B-HighNoise-Q5_K_M.gguf high-noise model (early steps) Q5_K_M · QuantStack/Wan2.2-T2V-A14B-GGUF | models/unet | 10.8 GB | Download → |
| Model | Wan2.2-T2V-A14B-LowNoise-Q5_K_M.gguf low-noise model (late steps) Q5_K_M · QuantStack/Wan2.2-T2V-A14B-GGUF | models/unet | 10.8 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 → |
| Total download · keep about the same free on disk | 28.6 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: FP8 workflow (.json) · GGUF Q5_K_M workflow (.json). Drag the file into ComfyUI. All workflows →
System RAM: 48 GB or more. These files (both noise models) take about 28.6 GB of RAM while loaded, 34.6 GB with Windows and ComfyUI. What happens with less. calculated
03Every Wan 2.2 T2V file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 28.6 GB | 32.9 GB | 16.9 GB too big | the original weights | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 14.3 GB | 18.6 GB | 2.6 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · QuantStack | 15.4 GB | 19.7 GB | 3.7 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 12.0 GB | 16.3 GB | spills 0.3 GB | very close to the original | Hugging Face → |
| Q5_K_M ← GGUF · QuantStack | 10.8 GB | 15.1 GB | fits · 0.9 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 9.7 GB | 14.0 GB | fits · 2.0 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 7.2 GB | 11.5 GB | fits · 4.5 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 5.3 GB | 9.6 GB | fits · 6.4 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 3.5 GB of working memory for Wan 2.2 T2V and a 0.8 GB reserve. Sizes 2026-10-01. Wan 2.2 A14B uses two files of this size (high-noise and low-noise); only one sits in VRAM at a time, both must fit in system RAM.
04Questions
How much VRAM does Wan 2.2 T2V need?
Around 9.6 GB with the smallest file (Q2_K) and 18.6 GB with an 8-bit file (FP8). The full 16-bit file needs about 32.9 GB.
Which Wan 2.2 T2V file should I download for the RTX 5060 Ti 16 GB?
Q5_K_M (10.8 GB) from QuantStack/Wan2.2-T2V-A14B-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for Wan 2.2 T2V on the RTX 5060 Ti 16 GB?
FP8 (14.3 GB): the RTX 5060 Ti 16 GB computes it natively, while Q8_0 (15.4 GB) is unpacked on the fly.
How much do I need to download for Wan 2.2 T2V on the RTX 5060 Ti 16 GB?
28.6 GB: Wan2.2-T2V-A14B-HighNoise-Q5_K_M.gguf, Wan2.2-T2V-A14B-LowNoise-Q5_K_M.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors.