How much VRAM does Wan 2.1 T2V 14B need?

At BF16 with everything resident, Wan 2.1 T2V 14B peaks at about 49.87 GB. Offloading components one at a time brings that to 39.29 GB, and quantising the weights to 4-bit brings it to 22.9 GB.

What is the cheapest GPU that runs Wan 2.1 T2V 14B?

The Arc A770 16GB at around $280, running it at GGUF Q8_0 with sequential offload, taking about 170 h 51 min per generation.

Wan · Diffusion transformer

Wan 2.1 T2V 14B

Wan 2.1 T2V 14B is a pipeline of 3 networks totalling 20.096 billion parameters, of which 14.288 billion do the actual generating. At its native 1280x720 and 81 frames the denoiser works on 75,600 latent tokens at once, and attention over that sequence is what sets the render time.

Whole pipeline20.096B

Denoiser14.288B

Download74.9 GB

Default steps50

Native length81 frames

Frame rate16 fps

What the pipeline is made of

This is the part that separates a media model from a language model: these are separate networks, and whether they sit in memory together is your choice, not the model's.

Component	Parameters	Published as	On disk	What it does
transformer	14.288B	F32	53.23 GB	Does the generating. Runs once per step, so it dominates both memory and time.
text_encoder	5.681B	F32	21.16 GB	Turns your prompt into conditioning. Runs once, then can leave the GPU entirely.
vae	0.127B	F32	0.47 GB	Converts between pixels and the compressed latent space. Small, but its decode pass is a memory spike.

These figures were measured from the weight file headers.

Memory by precision

Everything resident, at the model's native output size, on a card large enough that precision is the only constraint.

Precision	Peak VRAM	Quality	What it costs you
BF16	49.87 GB	100%	How the weights are published. No loss, and the largest footprint.
FP8	31.27 GB	97%	Halves the denoiser with a small, usually invisible cost. Needs Ada or newer.
GGUF Q8_0	32.43 GB	98%	Works on any card, unlike FP8. Slightly slower than native precision.
GGUF Q5_K_M	25.9 GB	95%	A middle step when Q8 will not fit.
GGUF Q4_K_M	23.91 GB	91%	The usual way a 12B image model gets onto an 8 GB card. Detail softens.
NF4	22.9 GB	89%	Aggressive 4-bit. Fast to load, noticeably looser on fine detail.

Memory by arrangement

The other lever: the same weights at the same precision, moved around differently. On this model quantising is the stronger lever: the denoiser is 71% of the pipeline and stays resident whatever you rearrange.

Arrangement	Peak VRAM	Time	How it works
Everything resident	49.87 GB	1 h 32 min	All components stay on the GPU. Fastest, and needs the most memory.
Text encoder on CPU	39.29 GB	1 h 36 min	The prompt is encoded once on the processor, so the encoder never touches the GPU at all. Standard practice on video models, whose encoders are often larger than their denoisers.
Component offload	39.05 GB	1 h 43 min	One component on the GPU at a time. The text encoder runs, then makes way for the denoiser. Costs a few seconds per generation.
Sequential offload	16.9 GB	9 h 13 min	Weights stream layer by layer from system RAM. Runs almost anything on almost anything, and is many times slower.

What a bigger output costs

Both memory and time climb faster than the frame count, because attention is quadratic in the length of the latent sequence.

Output	Latent tokens	Peak VRAM	Time
480p, 3 seconds	20,280	41.43 GB	9 min 43 s
480p, 5 seconds	32,760	43.33 GB	21 min 11 s
720p, 3 seconds	46,800	45.47 GB	39 min 5 s
720p, 5 seconds	75,600	49.87 GB	1 h 32 min
720p, 10 seconds	147,600	60.85 GB	5 h 22 min

Measured at BF16 with everything resident on an RTX 4090, so the columns compare with each other rather than with your machine.

Which hardware runs Wan 2.1 T2V 14B

74 of 118 consumer devices run it in some arrangement. Each row shows the fastest arrangement that fits on that device.

Device	Memory	Verdict	Arrangement	Peak	Time
RTX PRO 6000 Blackwell NVIDIA	96 GB	Barely usable	BF16 everything resident	49.87 GB	1 h 0 min
GeForce RTX 5090 NVIDIA	32 GB	Barely usable	GGUF Q5_K_M everything resident	25.9 GB	1 h 12 min
RTX 6000 Ada Generation NVIDIA	48 GB	Barely usable	GGUF Q8_0 everything resident	32.43 GB	1 h 23 min
GeForce RTX 4090 NVIDIA	24 GB	Barely usable	NF4 everything resident	22.9 GB	1 h 32 min
GeForce RTX 5090 Laptop NVIDIA	24 GB	Barely usable	NF4 everything resident	22.9 GB	1 h 57 min
NVIDIA DGX Spark 128GB NVIDIA	128 GB	Barely usable	BF16 everything resident	49.87 GB	2 h 1 min
GeForce RTX 3090 Ti NVIDIA	24 GB	Barely usable	NF4 everything resident	22.9 GB	3 h 10 min
RTX A6000 NVIDIA	48 GB	Barely usable	GGUF Q8_0 everything resident	32.43 GB	3 h 16 min
GeForce RTX 3090 NVIDIA	24 GB	Barely usable	NF4 everything resident	22.9 GB	3 h 34 min
RTX A5000 NVIDIA	24 GB	Barely usable	NF4 everything resident	22.9 GB	4 h 34 min
Jetson AGX Orin 64GB NVIDIA	64 GB	Barely usable	GGUF Q8_0 everything resident	32.43 GB	5 h 58 min
Radeon RX 7900 XTX AMD	24 GB	Barely usable	NF4 everything resident	22.9 GB	6 h 1 min
Radeon PRO W7900 AMD	48 GB	Barely usable	GGUF Q8_0 everything resident	32.43 GB	6 h 4 min
Jetson AGX Orin 32GB NVIDIA	32 GB	Barely usable	GGUF Q5_K_M text encoder on cpu	22.13 GB	6 h 16 min
GeForce RTX 5080 NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	13 h 32 min
GeForce RTX 4080 SUPER NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	14 h 38 min
GeForce RTX 4090 Laptop NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	15 h 13 min
GeForce RTX 4080 NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	15 h 37 min
GeForce RTX 5080 Laptop NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	16 h 1 min
GeForce RTX 5070 Ti NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	17 h 12 min
GeForce RTX 4070 Ti SUPER NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	17 h 18 min
Ryzen AI Max+ 395 64GB AMD	64 GB	Barely usable	GGUF Q8_0 everything resident	32.43 GB	18 h 49 min
Ryzen AI Max+ 395 96GB AMD	96 GB	Barely usable	BF16 everything resident	49.87 GB	18 h 49 min
Ryzen AI Max+ 395 128GB AMD	128 GB	Barely usable	BF16 everything resident	49.87 GB	18 h 49 min
Ryzen AI Max+ 395 32GB AMD	32 GB	Barely usable	GGUF Q5_K_M text encoder on cpu	22.13 GB	19 h 45 min
Apple M3 Ultra 96GB Apple	96 GB	Barely usable	BF16 everything resident	49.37 GB	28 h 50 min
Apple M3 Ultra 256GB Apple	256 GB	Barely usable	BF16 everything resident	49.37 GB	28 h 50 min
Apple M3 Ultra 512GB Apple	512 GB	Barely usable	BF16 everything resident	49.37 GB	28 h 50 min
Apple M2 Ultra 64GB Apple	64 GB	Barely usable	GGUF Q8_0 everything resident	31.93 GB	29 h 41 min
Apple M2 Ultra 128GB Apple	128 GB	Barely usable	BF16 everything resident	49.37 GB	29 h 41 min
Apple M2 Ultra 192GB Apple	192 GB	Barely usable	BF16 everything resident	49.37 GB	29 h 41 min
GeForce RTX 5060 Ti 16GB NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	31 h 43 min
GeForce RTX 4060 Ti 16GB NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	34 h 36 min
Apple M1 Ultra 64GB Apple	64 GB	Barely usable	GGUF Q8_0 everything resident	31.93 GB	38 h 27 min
Apple M1 Ultra 128GB Apple	128 GB	Barely usable	BF16 everything resident	49.37 GB	38 h 27 min
Ryzen AI 9 HX 370 32GB AMD	32 GB	Barely usable	GGUF Q5_K_M text encoder on cpu	22.13 GB	38 h 52 min
RTX A4000 NVIDIA	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	39 h 33 min
Radeon RX 7900 XT AMD	20 GB	Barely usable	BF16 sequential offload	16.9 GB	43 h 7 min
Apple M4 Max 36GB Apple	36 GB	Barely usable	GGUF Q4_K_M everything resident	23.41 GB	43 h 53 min
Apple M4 Max 48GB Apple	48 GB	Barely usable	GGUF Q8_0 everything resident	31.93 GB	43 h 53 min
Apple M4 Max 64GB Apple	64 GB	Barely usable	GGUF Q8_0 everything resident	31.93 GB	43 h 53 min
Apple M4 Max 128GB Apple	128 GB	Barely usable	BF16 everything resident	49.37 GB	43 h 53 min
Radeon RX 9070 XT AMD	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	45 h 47 min
Radeon RX 7900 GRE AMD	16 GB	Barely usable	GGUF Q8_0 sequential offload	14.81 GB	48 h 17 min
Apple M3 Max 36GB Apple	36 GB	Barely usable	GGUF Q4_K_M everything resident	23.41 GB	56 h 52 min

Source: Wan-AI/Wan2.1-T2V-14B-Diffusers . Downloaded 0.1 million times in the last month. See how we calculate, or browse every video model.