Datasheet for local AI49 models98 GPUsData read 2026-09-25
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Wan 2.2 Animate 14B VRAM requirements

Character animation and replacement: a reference image of a person is driven by the motion and face of another video.

Released 2025-09Licence: Apache-2.0Steps: 20Text encoder: UMT5-XXL + CLIP Vision H (6.7 GB as FP8)
TypeVideo
Parameters17.3B
Fits entirely from12 GB
8-bit or better from23 GB

01The files, and how much VRAM each needs

FileSizeNeededMin. VRAMQualitySource
16-bit34.5 GB39.8 GB40 GBthe original weightsComfy-Org/Wan_2.2_ComfyUI_Repackaged →
Q8_018.7 GB24.0 GB25 GBpractically identical to the originalQuantStack/Wan2.2-Animate-14B-GGUF →
FP817.3 GB22.6 GB23 GBpractically identical to the originalKijai/WanVideo_comfy_fp8_scaled →
INT818.4 GB23.7 GB24 GBpractically identical to the originalComfy-Org/Wan_2.2_ComfyUI_Repackaged →
Q6_K14.6 GB19.9 GB20 GBvery close to the originalQuantStack/Wan2.2-Animate-14B-GGUF →
Q5_K_M13.0 GB18.3 GB19 GBclose; small differences in fine detailQuantStack/Wan2.2-Animate-14B-GGUF →
Q4_K_M11.5 GB16.8 GB17 GBgood; some loss in fine detail and textQuantStack/Wan2.2-Animate-14B-GGUF →
Q3_K_M8.6 GB13.9 GB14 GBnoticeable loss of detailQuantStack/Wan2.2-Animate-14B-GGUF →
Q2_K6.5 GB11.8 GB12 GBheavy loss; a last resortQuantStack/Wan2.2-Animate-14B-GGUF →

“Needed” = file + 4.5 GB working memory + 0.8 GB system reserve.

03Best GPU for Wan 2.2 Animate

The cheapest cards (by launch price) that run it well, and every card sorted by memory: best GPU for Wan 2.2 Animate → Planning bigger images or longer clips? Open the calculator →

04By graphics card

GPUVRAMVerdictBest fileNeeded
Desktop graphics cards
RTX 2060 6 GB6 GBOffload onlyQ4_K_M16.8 GB
RTX 3050 6 GB6 GBOffload onlyQ4_K_M16.8 GB
RTX 2070 Super 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 2080 Super 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3050 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3060 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3060 Ti 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3070 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3070 Ti 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 4060 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 4060 Ti 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 5050 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 5060 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 5060 Ti 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RX 7600 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RX 9050 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RX 9060 XT 8 GB8 GBOffload onlyQ4_K_M16.8 GB
Arc B570 10 GB10 GBOffload onlyQ2_K11.8 GB
RTX 3080 10 GB10 GBOffload onlyQ2_K11.8 GB
RTX 2080 Ti 11 GB11 GBOffload onlyQ2_K11.8 GB
Arc B580 12 GB12 GBTightQ2_K11.8 GB
RTX 2060 12 GB12 GBTightQ2_K11.8 GB
RTX 3060 12 GB12 GBTightQ2_K11.8 GB
RTX 3080 12 GB12 GBTightQ2_K11.8 GB
RTX 3080 Ti 12 GB12 GBTightQ2_K11.8 GB
RTX 4070 12 GB12 GBTightQ2_K11.8 GB
RTX 4070 Super 12 GB12 GBTightQ2_K11.8 GB
RTX 4070 Ti 12 GB12 GBTightQ2_K11.8 GB
RTX 5070 12 GB12 GBTightQ2_K11.8 GB
RX 7700 XT 12 GB12 GBTightQ2_K11.8 GB
RX 9070 GRE 12 GB12 GBTightQ2_K11.8 GB
Arc A770 16 GB16 GBTightQ3_K_M13.9 GB
RTX 4060 Ti 16 GB16 GBTightQ3_K_M13.9 GB
RTX 4070 Ti Super 16 GB16 GBTightQ3_K_M13.9 GB
RTX 4080 16 GB16 GBTightQ3_K_M13.9 GB
RTX 4080 Super 16 GB16 GBTightQ3_K_M13.9 GB
RTX 5060 Ti 16 GB16 GBTightQ3_K_M13.9 GB
RTX 5070 Ti 16 GB16 GBTightQ3_K_M13.9 GB
RTX 5080 16 GB16 GBTightQ3_K_M13.9 GB
RX 7600 XT 16 GB16 GBTightQ3_K_M13.9 GB
RX 7800 XT 16 GB16 GBTightQ3_K_M13.9 GB
RX 7900 GRE 16 GB16 GBTightQ3_K_M13.9 GB
RX 9060 XT 16 GB16 GBTightQ3_K_M13.9 GB
RX 9070 16 GB16 GBTightQ3_K_M13.9 GB
RX 9070 XT 16 GB16 GBTightQ3_K_M13.9 GB
RX 7900 XT 20 GB20 GBRunsQ6_K19.9 GB
Arc Pro B60 24 GB24 GBRuns wellFP822.6 GB
RTX 3090 24 GB24 GBRuns wellFP822.6 GB
RTX 3090 Ti 24 GB24 GBRuns wellFP822.6 GB
RTX 4090 24 GB24 GBRuns wellFP822.6 GB
RX 7900 XTX 24 GB24 GBRuns wellFP822.6 GB
Arc Pro B70 32 GB32 GBRuns wellQ8_024.0 GB
RTX 5090 32 GB32 GBRuns wellFP822.6 GB
Laptop GPUs
RTX 3050 Laptop 4 GB4 GBNot practicalQ2_K11.8 GB
RTX 3050 Ti Laptop 4 GB4 GBNot practicalQ2_K11.8 GB
RTX 2060 Laptop 6 GB6 GBOffload onlyQ4_K_M16.8 GB
RTX 3050 Laptop 6 GB6 GBOffload onlyQ4_K_M16.8 GB
RTX 3060 Laptop 6 GB6 GBOffload onlyQ4_K_M16.8 GB
RTX 4050 Laptop 6 GB6 GBOffload onlyQ4_K_M16.8 GB
RTX 2070 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 2070 Super Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 2080 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 2080 Super Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3070 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3070 Ti Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 3080 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 4060 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 4070 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 5050 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 5060 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 5070 Laptop 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RX 7600M 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RX 7600M XT 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RX 7600S 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RX 7700S 8 GB8 GBOffload onlyQ4_K_M16.8 GB
RTX 4080 Laptop 12 GB12 GBTightQ2_K11.8 GB
RTX 5070 Laptop 12 GB12 GBTightQ2_K11.8 GB
RTX 5070 Ti Laptop 12 GB12 GBTightQ2_K11.8 GB
RX 7800M 12 GB12 GBTightQ2_K11.8 GB
RTX 3080 Laptop 16 GB16 GBTightQ3_K_M13.9 GB
RTX 3080 Ti Laptop 16 GB16 GBTightQ3_K_M13.9 GB
RTX 4090 Laptop 16 GB16 GBTightQ3_K_M13.9 GB
RTX 5080 Laptop 16 GB16 GBTightQ3_K_M13.9 GB
RX 7900M 16 GB16 GBTightQ3_K_M13.9 GB
RTX 5090 Laptop 24 GB24 GBRuns wellFP822.6 GB
Unified memory
Radeon 8060S (Strix Halo) 96 GB96 GBRuns well16-bit39.8 GB
Apple Silicon Macs (by memory)
Mac 16 GB12.7 GBTightQ2_K11.8 GB
Mac 18 GB14.4 GBTightQ3_K_M13.9 GB
Mac 24 GB19.6 GBRunsQ5_K_M18.3 GB
Mac 32 GB26.8 GBRuns wellQ8_024.0 GB
Mac 36 GB30.2 GBRuns wellQ8_024.0 GB
Mac 48 GB40.2 GBRuns well16-bit39.8 GB
Mac 64 GB55.7 GBRuns well16-bit39.8 GB
Mac 96 GB85 GBRuns well16-bit39.8 GB
Mac 128 GB115.4 GBRuns well16-bit39.8 GB
Mac 192 GB175.4 GBRuns well16-bit39.8 GB
Mac 256 GB236.9 GBRuns well16-bit39.8 GB
Mac 512 GB498.1 GBRuns well16-bit39.8 GB

On RTX 40/50 GPUs the FP8 file is preferred over Q8_0 when both fit (hardware FP8). All verdicts are calculated; see how the numbers work.

05Text encoder, VAE and other files

FileFolderSizeWhen
UMT5-XXL FP8 (scaled)
umt5_xxl_fp8_e4m3fn_scaled.safetensors
models/text_encoders6.7 GBtext encoder · smaller, recommendedDownload →
UMT5-XXL FP16
umt5_xxl_fp16.safetensors
models/text_encoders11.4 GBtext encoder · alternativeDownload →
Wan 2.1 VAE
wan_2.1_vae.safetensors
models/vae0.3 GBrequiredDownload →
CLIP Vision H
clip_vision_h.safetensors
models/clip_vision1.3 GBrequiredDownload →

The files the official ComfyUI workflows load next to the model. Sizes read from Hugging Face (2026-09-25). Every GPU page for this model lists the exact set to download for that card, with the total.

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 16 GB the FP8 encoder fits on its own, so prompt encoding stays fast.

06Where the files go in ComfyUI

FileFolderLoader node
Diffusion model (.safetensors: 16-bit, FP8, INT8)ComfyUI/models/diffusion_modelsLoad Diffusion Model
GGUF file (.gguf)ComfyUI/models/unetUnet Loader (GGUF) — from the ComfyUI-GGUF node pack
Text encoderComfyUI/models/text_encodersLoad CLIP / DualCLIPLoader (or the GGUF versions)
VAEComfyUI/models/vaeLoad VAE

Standard ComfyUI folders. After copying files, press R in ComfyUI (or restart it) to refresh the lists. Some uploads need their uploader's own loader node — see the notes above.

07AMD, Intel and NVIDIA: which file types are fast

File typeRTX 50RTX 40RTX 30 / 20RX 9000RX 7000/6000 · Strix HaloIntel Arc
16-bitRunsRunsRunsRunsRunsRuns
FP8Native FP8Native FP8No FP8 speed-upNative FP8No FP8 speed-upNo FP8 speed-up
INT8Native INT8Native INT8Native INT8Native INT8Native INT8No INT8 speed-up
GGUFRuns (GGUF node)Runs (GGUF node)Runs (GGUF node)Runs (GGUF node)Runs (GGUF node)Runs (GGUF node)

Every file type loads on every listed GPU, so the memory verdicts apply to all of them. What differs is speed: FP8 maths needs RTX 40/50 or RX 9000 (with ROCm 6.4+ and PyTorch 2.7+); ComfyUI's INT8 maths runs on NVIDIA and AMD, not on Intel; GGUF is unpacked on the fly on any GPU, which costs some speed. NVFP4 files are fast only on RTX 50. AMD runs ComfyUI on Windows through ROCm, Intel through PyTorch XPU; some custom nodes are NVIDIA-only. Source: ComfyUI model_management.py. AMD and Intel guide →

08Measured and reported results

No measured results yet. Send yours.

09Training a LoRA for Wan 2.2 Animate

No trainer documentation with a VRAM figure for this model was found yet. What the trainers say for other models →

10Every file tracked for Wan 2.2 Animate

FileTypeSizeRepo
wan2.2_animate_14B_bf16.safetensorsBF1634.55 GBComfy-Org/Wan_2.2_ComfyUI_Repackaged →
wan2.2_animate_14B_int8_convrot.safetensorsINT818.41 GBComfy-Org/Wan_2.2_ComfyUI_Repackaged →
Wan2_2-Animate-14B_fp8_scaled_e4m3fn_KJ_v2.safetensorsFP817.32 GBKijai/WanVideo_comfy_fp8_scaled →
Wan2_2-Animate-14B_fp8_scaled_e5m2_KJ_v2.safetensorsFP817.32 GBKijai/WanVideo_comfy_fp8_scaled →
Wan2.2-Animate-14B-Q8_0.ggufQ8_018.72 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q6_K.ggufQ6_K14.61 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q5_K_M.ggufQ5_K_M13.00 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q5_0.ggufQ5_012.53 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q5_K_S.ggufQ5_K_S12.35 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q4_K_M.ggufQ4_K_M11.50 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q4_K_S.ggufQ4_K_S10.59 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q4_0.ggufQ4_010.40 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q3_K_M.ggufQ3_K_M8.63 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q3_K_S.ggufQ3_K_S7.97 GBQuantStack/Wan2.2-Animate-14B-GGUF →
Wan2.2-Animate-14B-Q2_K.ggufQ2_K6.46 GBQuantStack/Wan2.2-Animate-14B-GGUF →

Character animation / replacement driven by a reference video (pose + face). Native WanAnimateToVideo node; pose/face preprocessing usually via custom nodes (e.g. Kijai ComfyUI-WanAnimatePreprocess). HF API size ~17.3B params. Single model (not high/low-noise pair).