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LatestTencentWan 2.2
TencentText → Video

Tencent Releases Wan2.2, a 14B MoE Video Model

The new Apache 2.0-licensed generator uses a Mixture-of-Experts architecture and is available in the popular Diffusers library format for easier integration.

Jul 28, 2025
NotableApache 2.0
Wan2.2 T2V A14B

Tencent has released Wan2.2, a powerful new text-to-video model, making it available to the public on the Hugging Face Hub. The model, officially named Wan2.2-T2V-A14B, stands out with its 14-billion-parameter scale and a sophisticated architecture, all provided under a permissive Apache 2.0 license.

An Efficient Architecture

At its core, Wan2.2 employs a Mixture-of-Experts (MoE) design. This approach allows the model to be very large in terms of total parameters while only activating a fraction of them for any given task. The result is more computationally efficient inference compared to a dense model of similar size. Its release in the popular Diffusers library format also simplifies integration for developers and researchers looking to build on the technology.

This release marks another significant contribution to the open-source AI video landscape. By providing a large, advanced model with a permissive license, Tencent is fueling the innovation happening outside of closed, proprietary systems. It provides a new foundation for developers creating applications in media, entertainment, and beyond, adding more competition and diversity to the field of generative video.

Sources

  • Wan-AI/Wan2.2-T2V-A14B-Diffusers

    Hugging Face

    Visit
OlderQwen Releases Wan2.2, a 5B Open-Source Video ModelQwen · Alibaba · Text → Video · last yearNewerAlibaba Releases Wan2.2, a 14B MoE Video ModelQwen · Alibaba · Image → Video · last year

Get the model

Hugging Face

Specs

Parameters14B · MoE
Resolution480P & 720P
Max duration5s
Size57.2 GB
PrecisionFP32
ArchitectureWanPipeline
LicenseAPACHE-2.0
Downloads3.6K
Likes595

Can you run it?

Runs on a laptop — about 15.3 GB at FP8.

  • FP32 (as published)

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    58.2 GB
  • 16-bit

    32 GB GPU (RTX 5090) · 48 GB Mac

    29.6 GB
  • FP8

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    15.3 GB
Your machine
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text → Video
2 versions — view changelog

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