Can the RTX 4070 Ti Super 16 GB run Wan 2.1 VACE 14B?
Download Q3_K_S (7.8 GB). With the model's working memory it needs about 12.6 GB, leaving 3.4 GB spare on 16 GB. Quality: noticeable loss of detail. For better quality, Q4_K_M (11.6 GB) also runs, with about 0.4 GB spilling into system RAM — a little slower, still practical.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.1_14B_VACE-Q3_K_S.gguf Q3_K_S · QuantStack/Wan2.1_14B_VACE-GGUF | models/unet | 7.8 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_2.1_ComfyUI_repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 14.8 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 14.8 GB, plus roughly 6 GB for Windows, ComfyUI and a browser: 20.8 GB in total. With less RAM it still runs, but Windows starts swapping to disk and loading gets very slow. calculated
02Every Wan VACE 14B file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 34.7 GB | 39.5 GB | 23.5 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 18.7 GB | 23.5 GB | 7.5 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 14.5 GB | 19.3 GB | 3.3 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 13.0 GB | 17.8 GB | spills 1.8 GB | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 11.6 GB | 16.4 GB | spills 0.4 GB | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_S ← GGUF · QuantStack | 7.8 GB | 12.6 GB | fits · 3.4 GB spare | noticeable loss of detail | 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: 11.4 GB as 16-bit, 6.7 GB as FP8, 3.7 GB as GGUF Q4_K_M. 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 4070 Ti Super 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.)
RTX 4070 Ti Super 16 GB: 16 GB GDDR6X · 256-bit · 672 GB/s · Ada Lovelace. Everything that runs on the RTX 4070 Ti Super 16 GB →
05What more VRAM would change
With 24 GB you could run Wan VACE 14B at 8-bit or better (Q8_0, 18.7 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 VACE 14B on a RTX 4070 Ti Super 16 GB, send your time per image and peak VRAM and it will appear here, credited.
07Questions
How much VRAM does Wan VACE 14B need?
Around 12.6 GB with the smallest file (Q3_K_S) and 23.5 GB with an 8-bit file (Q8_0), counting working memory and a small system reserve. The full 16-bit file needs about 39.5 GB.
Which Wan VACE 14B file should I download for the RTX 4070 Ti Super 16 GB?
Q3_K_S (7.8 GB) from QuantStack/Wan2.1_14B_VACE-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
Is FP8 faster than GGUF on the RTX 4070 Ti Super 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 VACE 14B on the RTX 4070 Ti Super 16 GB?
About 14.8 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.