Will It Run AI
video-generation, vram-requirements, wan-video, fp8, gguf, comfyui, alibaba

Wan 2.1 / 2.2 VRAM Requirements — 1.3B, 5B, 14B Variant GPU Guide (2026)

Wan Video 2.2 VRAM: 1.3B needs 4–6 GB (GGUF), 5B TI2V 8–12 GB, 14B 6–24 GB (FP8). Recommended GPUs and T5 offloading guide for every tier.

Wan Video is Alibaba's open-source video generation family and the quality benchmark for consumer-accessible video AI. The line spans four practical variants: 1.3B for budget GPUs, 5B TI2V for mid-range, and the flagship 14B that reaches the highest quality through either FP8 on a 24 GB GPU or GGUF with CPU offloading on 12 GB.

This guide covers the exact Wan 2.1 and Wan 2.2 VRAM requirements across every variant, at every precision level, with recommended hardware for each.

Quick orientation: Wan 2.2 improves on 2.1 with better motion quality and temporal coherence but has the same VRAM profile. Numbers apply to both generations unless noted.

The Wan Video Family at a Glance

VariantParamsModalityT.E. ParamsLicenseCatalog slug
Wan 2.1 T2V-1.3B1.3BText-to-videoT5-XXL 9.4BApache 2.0wan-video-2-1-1-3b
Wan 2.1 T2V-14B14BText-to-videoT5-XXL 9.4BApache 2.0wan-video-2-1-14b
Wan 2.2 T2V-14B (A14B)14B activeText-to-videoT5-XXL 9.4BApache 2.0wan-video-2-2-14b
Wan 2.2 TI2V-5B5BT+Image-to-video4.7BApache 2.0wan-video-2-2-ti2v-5b

All variants are Apache 2.0 licensed — the most permissive option in the open video model space, including for commercial use.

VRAM Requirements — Full Table

Wan 2.1 / 2.2 T2V-14B (flagship variant)

The T5-XXL text encoder (~9.4B parameters, ~9.4 GB at FP16) dominates the VRAM budget. The key strategy is offloading it to CPU RAM.

PrecisionT.E. locationVRAM (720p)VRAM (480p)Min GPU
FP16 fullOn GPU~54–65 GB~45–55 GBMulti-GPU / datacenter
FP8 (transformer)On GPU~22–26 GB~18–22 GBRTX 4090 24GB
FP8 (transformer)CPU offload~14–16 GB~12–14 GBRTX 4080 Super 16GB
GGUF Q5 (transformer)CPU offload~8–10 GB~6–8 GBRTX 4070 12GB
GGUF Q4 (transformer)CPU offload~7–9 GB~5–7 GBRTX 4060 Ti 16GB, RTX 4070 12GB

Wan 2.2 TI2V-5B

PrecisionT.E. locationVRAM (720p)VRAM (480p)Min GPU
FP16 fullOn GPU~22–28 GB~18–22 GBRTX 4090 24GB
FP8On GPU~12–15 GB~10–12 GBRTX 4080 Super 16GB
FP8CPU offload~8–10 GB~6–8 GBRTX 4070 12GB

Wan 2.1 T2V-1.3B (budget variant)

PrecisionVRAMNotes
FP16~9–13 GBT5 offload recommended even at FP16
GGUF Q5~5–7 GBT5 offload to CPU — runs well on 8 GB GPUs
GGUF Q4~4–6 GBMinimum viable configuration

Spec source: Param counts (1.3B, 5B, 14B), T5 text encoder size (9.4B for 14B/1.3B, 4.7B for TI2V-5B), max frames (81), and max resolution (720p) are VERIFIED from the diffusion catalog entries and Wan-AI HuggingFace model cards. VRAM estimates are derived from catalog data and community benchmarks — treat as reliable guidance with ±1–2 GB margin.

The T5 Offload Strategy

The single most impactful optimization for Wan Video 14B on consumer hardware is offloading the T5-XXL text encoder to CPU RAM. Here is why it works so well:

  • T5-XXL weighs 9.4B parameters — approximately 9.4 GB at FP16
  • During video generation, T5 is only used for the conditioning pass at the start of the denoising loop
  • After that initial pass, T5 sits idle while the DiT transformer does the actual work
  • Moving it to CPU RAM removes ~9 GB from GPU VRAM for the majority of the generation time

The trade-off: the CPU-to-GPU transfer at conditioning adds approximately 10–20 seconds to the start of each generation (depending on system RAM speed and PCIe bandwidth). This is a one-time cost per prompt, not per step.

System RAM requirement for T5 offload: minimum 24 GB RAM (T5 is ~9 GB; leave room for the OS and ComfyUI). 32 GB RAM is strongly recommended.

GPU Tier Guide

8 GB — RTX 4060 8GB, RTX 4060 Ti 8GB

Wan 14B is very difficult at 8 GB — only GGUF Q4 with T5 CPU offload comes close, and even then at 480p only. The practical choice is:

  • Wan 2.1 1.3B GGUF Q4: 4–6 GB VRAM. Excellent for budget setups. Short clips, 480p, decent motion quality for its size.

Verdict: Stay with the 1.3B variant at this tier. The 14B is technically possible with GGUF+offload on 8 GB but generation times become impractical (20+ min/clip).

12 GB — RTX 4070 12GB, RTX 4070 Super 12GB, RTX 3060 12GB

This tier unlocks the 14B quality tier:

  • Wan 2.2 14B GGUF Q4/Q5 + T5 CPU offload: ~6–8 GB VRAM at 480p. The quality step from 1.3B to 14B is dramatic.
  • Wan 2.2 TI2V-5B FP8: ~8–10 GB. Image-to-video with strong quality at 480p.
  • Wan 2.1 1.3B: Runs comfortably, leaving headroom for higher frame counts.

The RTX 4070 12GB is a surprisingly capable video generation card because of the T5 offloading strategy. GGUF Q5 gives slightly better quality than Q4 with minimal VRAM cost.

Verdict: The sweet spot for Wan 14B. GGUF + T5 CPU offload makes this tier genuinely useful for creative video work. The RTX 4070 Super 12GB is the better choice over the base 4070 due to faster memory bandwidth.

16 GB — RTX 4060 Ti 16GB, RTX 4070 Ti Super 16GB, RTX 4080 Super 16GB

Full flexibility at 16 GB:

  • Wan 2.2 14B FP8 + T5 CPU offload: ~14–16 GB VRAM at 720p. Best quality available on consumer hardware without a 24 GB card.
  • Wan 2.2 TI2V-5B FP8: Fits entirely on GPU. Fast generation at 720p.
  • Wan 2.2 14B GGUF Q5: Headroom for 720p at higher frame counts.

The RTX 4080 Super 16GB is the best 16 GB option for Wan Video — its wider 256-bit memory bus makes a real difference in generation speed versus the RTX 4060 Ti 16GB (128-bit).

Verdict: 16 GB with the RTX 4080 Super is the practical "no real compromises" tier for Wan 14B. FP8 + T5 offload at 720p generates in 2–4 minutes per clip.

24 GB — RTX 4090, RTX 3090

The uncompromised tier:

  • Wan 2.2 14B FP8, T.E. on GPU: ~22–26 GB VRAM. No offloading, no waiting for CPU transfers. Full 720p generation.
  • Wan 2.1 14B FP8: Same VRAM profile, slightly lower quality output than 2.2.

The RTX 4090 24GB can run Wan 14B at FP8 with the T5 encoder on GPU. Generation time is typically 60–120 seconds per 4-second clip at 50 steps, 720p.

Verdict: The RTX 4090 24GB is the recommended card for production Wan 14B workflows. FP8 with T5 on GPU gives the best quality-per-speed ratio.

Apple Silicon Macs

MacMemoryConfigEst. speed (14B FP8)
M4 Max 36GB36 GB unifiedFP8, T.E. on memory~3–5 min/clip
M4 Max 48GB48 GB unifiedFP16 14B (tight) or FP8 with headroom~2–4 min/clip
M4 Max 64GB64 GB unifiedFP16 14B comfortably~2–3 min/clip

Apple Silicon generation is 2–4× slower than NVIDIA at equivalent effective precision but unified memory means no CPU offloading tricks are needed for T5.

Comparing Wan 2.1 vs 2.2 at Each Tier

Use caseRecommended variantReason
8 GB GPU, budgetWan 2.1 1.3B GGUFFastest at 480p, excellent VRAM fit
12 GB GPU, qualityWan 2.2 14B GGUF + T5 CPULarge quality jump over 1.3B
16 GB GPU, T+I workflowWan 2.2 TI2V-5B FP8Native image-to-video, strong at 720p
24 GB GPU, max qualityWan 2.2 14B FP8Best open video model at this tier
Commercial useWan 2.2 TI2V-5B or any variantAll Wan models are Apache 2.0

Wan Video in ComfyUI

ComfyUI is the recommended frontend for Wan Video 14B, particularly for the T5 CPU offload workflow:

  1. Install ComfyUI with the ComfyUI-WanVideoWrapper or ComfyUI-VideoHelperSuite custom nodes
  2. Download the GGUF quantized transformer checkpoint (available on Hugging Face and CivitAI)
  3. Load the T5-XXL encoder separately with CPU offload enabled
  4. Use a community workflow JSON for your VRAM tier — most popular workflows are pre-tuned for 12 GB and 24 GB cards

The GGUF Wan 14B ComfyUI workflow is one of the most widely tested consumer video generation setups available. Community benchmarks show consistent results on RTX 4070 12GB at 480p with ~8 GB peak VRAM.

Related Guides

Frequently Asked Questions

How much VRAM does Wan Video 2.2 14B need?

Wan Video 2.2 14B is a 14B 3D DiT model with a 9.4B T5-XXL text encoder. At FP16 with the text encoder on GPU, the full pipeline requires approximately 54–65 GB VRAM — datacenter territory. With GGUF quantization on the transformer and T5 offloaded to CPU RAM, it drops to approximately 6–8 GB GPU VRAM, fitting on 12 GB consumer GPUs like the RTX 4070 or RTX 3060 12GB. FP8 (T.E. on GPU) needs about 22–26 GB.

What is the difference between Wan 2.1 and Wan 2.2?

Wan Video 2.2 14B is the improved successor to 2.1 14B. Both share the same 3D DiT architecture and VRAM profile, but 2.2 offers better temporal coherence, sharper motion transitions, and improved prompt adherence. Wan 2.2 also introduces the TI2V-5B variant — a text-and-image-to-video model at 5B parameters. If you are starting fresh, Wan 2.2 is the better choice. VRAM requirements are essentially the same.

Can Wan Video 14B run on an 8 GB GPU?

With aggressive quantization and CPU offloading, Wan Video 14B can technically run on 8 GB VRAM. Use GGUF Q4 on the transformer and offload the T5 text encoder (9.4B, ~9.4 GB at FP16) entirely to CPU RAM. Expect only 480p output, very slow generation times (10–20+ minutes per clip), and at least 32 GB system RAM. For 8 GB GPUs, the 1.3B variant is a far better fit.

What is Wan Video 2.2 TI2V-5B?

Wan Video 2.2 TI2V-5B is a 5B text-and-image-to-video model. It takes a text prompt plus a reference image and generates a video extending or animating that image. At FP8 it needs approximately 8–12 GB VRAM, making it the best balance between quality and accessibility in the Wan family. It is licensed Apache 2.0, making it the most permissively licensed Wan video model.

Does Wan Video 2.2 support image-to-video?

Yes. The Wan Video 2.2 family includes dedicated I2V (image-to-video) variants alongside the standard T2V (text-to-video) models. The 5B TI2V variant is explicitly designed for text+image-to-video. The 14B models also ship with separate T2V and I2V checkpoint variants. Check the HuggingFace Wan-AI repository for the specific variant download.

How do I offload the T5 text encoder for Wan Video?

In ComfyUI, use the t5_cpu workflow mode — this tells the T5 encoder to live in system RAM and transfer only the conditioning output to GPU. In diffusers, use pipe.enable_sequential_cpu_offload() after model loading. Either way, you need at least 24 GB system RAM (the T5-XXL encoder is 9.4 GB at FP16). The trade-off is ~15–25% slower generation versus having T5 on GPU.