Can the RTX 3080 10 GB run Wan 2.1 VACE 14B?
Not entirely. Even Q4_K_M is 6.4 GB over 10 GB, so ComfyUI keeps part of it in system RAM.
The smallest sensible file, Q4_K_M (11.6 GB), needs about 16.4 GB - 6.4 GB more than RTX 3080 10 GB has. ComfyUI can still run it by streaming part of the model from system RAM. How much slower that is depends on your ComfyUI version, the file format and the PCIe link - see the note below.
01With more VRAM
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.
What changes from the RTX 3080 10 GB to the RTX 3090 24 GB → · What changes from the RTX 3080 10 GB to the RTX 5070 Ti 16 GB →
02Every Wan VACE 14B file on 10 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 34.7 GB | 39.5 GB | 29.5 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 18.7 GB | 23.5 GB | 13.5 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 14.5 GB | 19.3 GB | 9.3 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 13.0 GB | 17.8 GB | 7.8 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M ← GGUF · QuantStack | 11.6 GB | 16.4 GB | 6.4 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_S GGUF · QuantStack | 7.8 GB | 12.6 GB | 2.6 GB too big | noticeable loss of detail | Hugging Face → |
“Needed” adds 4 GB of working memory for Wan VACE 14B and a 0.8 GB reserve. Sizes 2026-10-01.
03What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | Wan2.1_14B_VACE-Q4_K_M.gguf Q4_K_M · QuantStack/Wan2.1_14B_VACE-GGUF | models/unet | 11.6 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_2.1_ComfyUI_repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 18.6 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
04Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan VACE 14B on a RTX 3080 10 GB, send your time per image and peak VRAM and it will appear here, credited.
05About the RTX 3080 10 GB
The RTX 3080 10 GB (Ampere) has no FP8 compute. FP8 files still load and save memory, but ComfyUI converts them back before the maths, so there is no speed gain - Q8_0 GGUF is the closer-to-original 8-bit choice. ComfyUI can compute INT8 files natively on NVIDIA, which is worth a try where a model offers one.
10 GB GDDR6X · 320-bit · 760 GB/s · Ampere. Everything that runs on it →
06Questions
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). The full 16-bit file needs about 39.5 GB.
Which Wan VACE 14B file should I download for the RTX 3080 10 GB?
Q4_K_M (11.6 GB) from QuantStack/Wan2.1_14B_VACE-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
How much do I need to download for Wan VACE 14B on the RTX 3080 10 GB?
18.6 GB: Wan2.1_14B_VACE-Q4_K_M.gguf, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors.