Can the RTX 3080 10 GB run Wan 2.2 I2V A14B?
Download Q2_K (5.3 GB). With the model's working memory it needs about 9.6 GB, leaving 0.4 GB spare on 10 GB. Quality: heavy loss; a last resort. For better quality, Q3_K_M (7.2 GB) also runs, with about 1.5 GB spilling into system RAM — a little slower, still practical.
Worth trying on this GPU: with an up-to-date ComfyUI, Dynamic VRAM (on by default for NVIDIA since March 2026) streams whatever does not fit from system RAM, and ComfyUI's own start-up message recommends native FP8/INT8 files over GGUF, saying they “will be faster even if they are larger than your memory”. So before settling for a heavily compressed GGUF, try the native FP8 file (14.3 GB). The file recommended above is the best one that fits entirely — the safe choice on older ComfyUI versions, AMD and Intel. Comfy blog: 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 3080 10 GB, start with blocks_to_swap ≈ 26 (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 | Wan2.2-I2V-A14B-HighNoise-Q2_K.gguf high-noise model (early steps) Q2_K · QuantStack/Wan2.2-I2V-A14B-GGUF | models/unet | 5.3 GB | Download → |
| Model | Wan2.2-I2V-A14B-LowNoise-Q2_K.gguf low-noise model (late steps) Q2_K · QuantStack/Wan2.2-I2V-A14B-GGUF | models/unet | 5.3 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_2.2_ComfyUI_Repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 17.6 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 files, 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 17.6 GB, plus roughly 6 GB for Windows, ComfyUI and a browser: 23.6 GB in total. With less RAM it still runs, but Windows starts swapping to disk and loading gets very slow. calculated
02Every Wan 2.2 I2V file on 10 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 28.6 GB | 32.9 GB | 22.9 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 15.4 GB | 19.7 GB | 9.7 GB too big | practically identical to the original | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 14.3 GB | 18.6 GB | 8.6 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 12.0 GB | 16.3 GB | 6.3 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 10.8 GB | 15.1 GB | 5.1 GB too big | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 9.7 GB | 14.0 GB | 4.0 GB too big | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 7.2 GB | 11.5 GB | spills 1.5 GB | noticeable loss of detail | Hugging Face → |
| Q2_K ← GGUF · QuantStack | 5.3 GB | 9.6 GB | fits · 0.4 GB spare | heavy loss; a last resort | Hugging Face → |
Sizes from the Hugging Face file listing, read 2026-09-25. “Needed” = file + 3.5 GB working memory for this model + 0.8 GB kept free for the system. 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.
03The text encoder
UMT5-XXL + CLIP Vision H: 11.4 GB as 16-bit, 6.7 GB as FP8, 3.7 GB as GGUF Q4_K_M (plus CLIP Vision H (1.3 GB) for the input image). 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 20 GB you could run Wan 2.2 I2V at 8-bit or better (Q8_0, 15.4 GB) with no offloading.
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 2.2 I2V 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 2.2 I2V need?
Around 9.6 GB with the smallest file (Q2_K) and 18.6 GB with an 8-bit file (FP8), counting working memory and a small system reserve. The full 16-bit file needs about 32.9 GB.
Which Wan 2.2 I2V file should I download for the RTX 3080 10 GB?
Q2_K (5.3 GB) from QuantStack/Wan2.2-I2V-A14B-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 2.2 I2V on the RTX 3080 10 GB?
About 17.6 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.