Wan 2.2 I2V A14B VRAM requirements
The image-to-video Wan 2.2, and the most downloaded Wan 2.2 GGUF. Same two-model setup as the text-to-video version.
TypeVideo
Parameters14B
Fits entirely from10 GB
8-bit or better from19 GB
01The files, and how much VRAM each needs
| File | Size | Needed | Min. VRAM | Quality | Source |
|---|---|---|---|---|---|
| 16-bit | 28.6 GB | 32.9 GB | 33 GB | the original weights | Comfy-Org/Wan_2.2_ComfyUI_Repackaged → |
| Q8_0 | 15.4 GB | 19.7 GB | 20 GB | practically identical to the original | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| FP8 | 14.3 GB | 18.6 GB | 19 GB | practically identical to the original | Comfy-Org/Wan_2.2_ComfyUI_Repackaged → |
| Q6_K | 12.0 GB | 16.3 GB | 17 GB | very close to the original | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Q5_K_M | 10.8 GB | 15.1 GB | 16 GB | close; small differences in fine detail | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Q4_K_M | 9.7 GB | 14.0 GB | 14 GB | good; some loss in fine detail and text | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Q3_K_M | 7.2 GB | 11.5 GB | 12 GB | noticeable loss of detail | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Q2_K | 5.3 GB | 9.6 GB | 10 GB | heavy loss; a last resort | QuantStack/Wan2.2-I2V-A14B-GGUF → |
“Needed” = file + 3.5 GB working memory + 0.8 GB system reserve. Wan 2.2 A14B uses two files of this size; only one sits in VRAM at a time, both must fit in system RAM.
02By amount of VRAM
03Best GPU for Wan 2.2 I2V
The cheapest cards (by launch price) that run it well, and every card sorted by memory: best GPU for Wan 2.2 I2V → Planning bigger images or longer clips? Open the calculator →
04By graphics card
| GPU | VRAM | Verdict | Best file | Needed |
|---|---|---|---|---|
| Desktop graphics cards | ||||
| RTX 2060 6 GB | 6 GB | Offload only | Q4_K_M | 14.0 GB |
| RTX 3050 6 GB | 6 GB | Offload only | Q4_K_M | 14.0 GB |
| RTX 2070 Super 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 2080 Super 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3050 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3060 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3060 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3070 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3070 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 4060 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 4060 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 5050 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 5060 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 5060 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RX 7600 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RX 9050 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RX 9060 XT 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| Arc B570 10 GB | 10 GB | Tight | Q2_K | 9.6 GB |
| RTX 3080 10 GB | 10 GB | Tight | Q2_K | 9.6 GB |
| RTX 2080 Ti 11 GB | 11 GB | Tight | Q2_K | 9.6 GB |
| Arc B580 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 2060 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 3060 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 3080 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 3080 Ti 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 4070 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 4070 Super 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 4070 Ti 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 5070 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RX 7700 XT 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RX 9070 GRE 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| Arc A770 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 4060 Ti 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 4070 Ti Super 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 4080 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 4080 Super 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 5060 Ti 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 5070 Ti 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 5080 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 7600 XT 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 7800 XT 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 7900 GRE 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 9060 XT 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 9070 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 9070 XT 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 7900 XT 20 GB | 20 GB | Runs well | Q8_0 | 19.7 GB |
| Arc Pro B60 24 GB | 24 GB | Runs well | Q8_0 | 19.7 GB |
| RTX 3090 24 GB | 24 GB | Runs well | Q8_0 | 19.7 GB |
| RTX 3090 Ti 24 GB | 24 GB | Runs well | Q8_0 | 19.7 GB |
| RTX 4090 24 GB | 24 GB | Runs well | FP8 | 18.6 GB |
| RX 7900 XTX 24 GB | 24 GB | Runs well | Q8_0 | 19.7 GB |
| Arc Pro B70 32 GB | 32 GB | Runs well | Q8_0 | 19.7 GB |
| RTX 5090 32 GB | 32 GB | Runs well | FP8 | 18.6 GB |
| Laptop GPUs | ||||
| RTX 3050 Laptop 4 GB | 4 GB | Not practical | Q2_K | 9.6 GB |
| RTX 3050 Ti Laptop 4 GB | 4 GB | Not practical | Q2_K | 9.6 GB |
| RTX 2060 Laptop 6 GB | 6 GB | Offload only | Q4_K_M | 14.0 GB |
| RTX 3050 Laptop 6 GB | 6 GB | Offload only | Q4_K_M | 14.0 GB |
| RTX 3060 Laptop 6 GB | 6 GB | Offload only | Q4_K_M | 14.0 GB |
| RTX 4050 Laptop 6 GB | 6 GB | Offload only | Q4_K_M | 14.0 GB |
| RTX 2070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 2070 Super Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 2080 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 2080 Super Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3070 Ti Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 3080 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 4060 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 4070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 5050 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 5060 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 5070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RX 7600M 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RX 7600M XT 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RX 7600S 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RX 7700S 8 GB | 8 GB | Offload only | Q2_K | 9.6 GB |
| RTX 4080 Laptop 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 5070 Laptop 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 5070 Ti Laptop 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RX 7800M 12 GB | 12 GB | Tight | Q3_K_M | 11.5 GB |
| RTX 3080 Laptop 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 3080 Ti Laptop 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 4090 Laptop 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 5080 Laptop 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RX 7900M 16 GB | 16 GB | Runs | Q5_K_M | 15.1 GB |
| RTX 5090 Laptop 24 GB | 24 GB | Runs well | FP8 | 18.6 GB |
| Unified memory | ||||
| Radeon 8060S (Strix Halo) 96 GB | 96 GB | Runs well | 16-bit | 32.9 GB |
| Radeon 8065S (Gorgon Halo) 160 GB | 160 GB | Runs well | 16-bit | 32.9 GB |
| Apple Silicon Macs (by memory) | ||||
| Mac 16 GB | 12.7 GB | Tight | Q3_K_M | 11.5 GB |
| Mac 18 GB | 14.4 GB | Runs | Q4_K_M | 14.0 GB |
| Mac 24 GB | 19.6 GB | Runs | Q6_K | 16.3 GB |
| Mac 32 GB | 26.8 GB | Runs well | Q8_0 | 19.7 GB |
| Mac 36 GB | 30.2 GB | Runs well | Q8_0 | 19.7 GB |
| Mac 48 GB | 40.2 GB | Runs well | 16-bit | 32.9 GB |
| Mac 64 GB | 55.7 GB | Runs well | 16-bit | 32.9 GB |
| Mac 96 GB | 85 GB | Runs well | 16-bit | 32.9 GB |
| Mac 128 GB | 115.4 GB | Runs well | 16-bit | 32.9 GB |
| Mac 192 GB | 175.4 GB | Runs well | 16-bit | 32.9 GB |
| Mac 256 GB | 236.9 GB | Runs well | 16-bit | 32.9 GB |
| Mac 512 GB | 498.1 GB | Runs well | 16-bit | 32.9 GB |
05Text encoder, VAE and other files
| File | Folder | Size | When | |
|---|---|---|---|---|
| UMT5-XXL FP8 (scaled) umt5_xxl_fp8_e4m3fn_scaled.safetensors | models/text_encoders | 6.7 GB | text encoder · smaller, recommended | Download → |
| UMT5-XXL FP16 umt5_xxl_fp16.safetensors | models/text_encoders | 11.4 GB | text encoder · alternative | Download → |
| Wan 2.1 VAE wan_2.1_vae.safetensors | models/vae | 0.3 GB | required | Download → |
The files the official ComfyUI workflows load next to the model. Sizes read from Hugging Face (2026-10-01).
06Where the files go in ComfyUI
| File | Folder | Loader node |
|---|---|---|
| Diffusion model (.safetensors: 16-bit, FP8, INT8) | ComfyUI/models/diffusion_models | Load Diffusion Model |
| GGUF file (.gguf) | ComfyUI/models/unet | Unet Loader (GGUF) - from the ComfyUI-GGUF node pack |
| Text encoder | ComfyUI/models/text_encoders | Load CLIP / DualCLIPLoader (or the GGUF versions) |
| VAE | ComfyUI/models/vae | Load VAE |
07Measured and reported results
| Label | GPU | Setup | Result | Peak VRAM | Date | Source |
|---|---|---|---|---|---|---|
| reported | RTX 3060 12 GB | video_wan2_2_14B_i2v template (quant not stated) · 720p “53frame/16fpsで513.5秒” ComfyUI on native Linux, PyTorch 2.8; 10GB offloaded via MultiGPU node; steps/LoRA not stated | 513.5 s / clip (53 frames) | — | 2025-11-26 | note.com → |
| reported | RTX 4060 Laptop 8 GB | WAN 2.2 14B Rapid distilled (all-in-one) · 480x480 · 4 steps “4/4 [00:45<00:00, 11.46s/it] ... Prompt executed in 111.41 seconds” Same machine note as above (8 GB, likely Laptop); 4,569 MB loaded on GPU with 11,067 MB offloaded (peak_vram = loaded weights, not measured peak) | 111.41 s / clip (33 frames) · 11.46 s/it | 4.46 GB | 2026-03-06 | lilting.ch → |
| reported | RTX 4090 24 GB | Wan2.2-I2V-A14B High/Low Q6_K GGUF · 800x448 · 8 steps “RTX 4090なら1分半ほど、RTX 5080はほぼ2分で480p解像度を5秒出力できます。” Chimolog GPU review; ComfyUI 0.3.5x, Kijai-based workflow, 2+2+4 steps with Lightx2v/Lightning LoRAs; '1分半ほど' = about 1.5 min (converted to 90 s, approximate); exact values only in | 90 s / clip (81 frames) | — | 2025-08-28 | chimolog.co → |
| reported | RTX 5060 Ti 16 GB | video_wan2_2_14B_i2v template (quant not stated) · 720p “RTX5060ti 16GBだと165.9秒なので、2〜3倍時間が必要ですが、落ちずに720p動画が生成できる事はたいしたものです。” Comparison figure given in the RTX 3060 post, presumably same 720p/53-frame test (not explicitly restated); steps not stated | 165.9 s / clip (53 frames) | — | 2025-11-26 | note.com → |
Other people's numbers are copied as published, with a link. Send yours.
08Training a LoRA for Wan 2.2 I2V
| Trainer | VRAM | Type | Settings and quote | Source |
|---|---|---|---|---|
| SimpleTuner | 16 GB | stated minimum | General Wan guide statement listed right after the Wan 2.2 I2V section; no settings given “a realistic minimum is 16GB or, a single 3090 or V100 GPU” | github.com → |
reported From each trainer's own docs, 2026-09-25. Why the figures differ →
09Every file tracked for Wan 2.2 I2V
| File | Type | Size | Repo |
|---|---|---|---|
| wan2.2_i2v_high_noise_14B_fp16.safetensors (high_noise) | F16 | 28.58 GB | Comfy-Org/Wan_2.2_ComfyUI_Repackaged → |
| wan2.2_i2v_low_noise_14B_fp16.safetensors (low_noise) | F16 | 28.58 GB | Comfy-Org/Wan_2.2_ComfyUI_Repackaged → |
| wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors (high_noise) | FP8 | 14.29 GB | Comfy-Org/Wan_2.2_ComfyUI_Repackaged → |
| wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors (low_noise) | FP8 | 14.29 GB | Comfy-Org/Wan_2.2_ComfyUI_Repackaged → |
| Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf (high_noise) | Q8_0 | 15.41 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf (low_noise) | Q8_0 | 15.41 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q6_K.gguf (high_noise) | Q6_K | 12.00 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q6_K.gguf (low_noise) | Q6_K | 12.00 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q5_1.gguf (high_noise) | Q5_1 | 11.02 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q5_1.gguf (low_noise) | Q5_1 | 11.02 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q5_K_M.gguf (high_noise) | Q5_K_M | 10.79 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q5_K_M.gguf (low_noise) | Q5_K_M | 10.79 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q5_0.gguf (high_noise) | Q5_0 | 10.31 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q5_0.gguf (low_noise) | Q5_0 | 10.31 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q5_K_S.gguf (high_noise) | Q5_K_S | 10.14 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q5_K_S.gguf (low_noise) | Q5_K_S | 10.14 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q4_K_M.gguf (high_noise) | Q4_K_M | 9.65 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q4_K_M.gguf (low_noise) | Q4_K_M | 9.65 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q4_1.gguf (low_noise) | Q4_1 | 9.26 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q4_K_S.gguf (high_noise) | Q4_K_S | 8.75 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q4_K_S.gguf (low_noise) | Q4_K_S | 8.75 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q4_0.gguf (low_noise) | Q4_0 | 8.56 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q3_K_M.gguf (high_noise) | Q3_K_M | 7.18 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q3_K_M.gguf (low_noise) | Q3_K_M | 7.18 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q3_K_S.gguf (high_noise) | Q3_K_S | 6.52 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q3_K_S.gguf (low_noise) | Q3_K_S | 6.52 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-HighNoise-Q2_K.gguf (high_noise) | Q2_K | 5.30 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
| Wan2.2-I2V-A14B-LowNoise-Q2_K.gguf (low_noise) | Q2_K | 5.30 GB | QuantStack/Wan2.2-I2V-A14B-GGUF → |
BONUS (not requested): the I2V A14B GGUF is ~2x more downloaded than T2V. Same high/low-noise two-model setup as T2V A14B.