Can the RTX 3080 10 GB run Wan 2.1 VACE 14B?
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.
01What 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). 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 18.6 GB, plus roughly 6 GB for Windows, ComfyUI and a browser: 24.6 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 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 → |
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 10 GB the FP8 encoder fits on its own, so prompt encoding stays fast.
04About this GPU
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.
RTX 3080 10 GB: 10 GB GDDR6X · 320-bit · 760 GB/s · Ampere. Everything that runs on the RTX 3080 10 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.
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 →
06Measured 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.
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 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.
Is FP8 faster than GGUF on the RTX 3080 10 GB?
No. This GPU has no FP8 compute, so ComfyUI converts FP8 weights back before the maths. FP8 only saves memory here; Q8_0 GGUF is the closer-to-original 8-bit pick.
How much do I need to download for Wan VACE 14B on the RTX 3080 10 GB?
About 18.6 GB for the model file, text encoder and VAE listed on this page, and the same again free on disk. With a 10 GB card, 32 GB of system RAM or more is recommended.