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LatestQwen · AlibabaWan2.2-T2V-A14B
Qwen · AlibabaText → Video

Qwen Unveils Wan2.2, a 14B Open Text-to-Video Model

The new Apache 2.0-licensed model from Alibaba's team uses a Mixture-of-Experts architecture for efficient, high-quality video generation.

Jul 24, 2025
NotableApache 2.0
Wan2.2 T2V A14B

The Qwen team at Alibaba has introduced a significant new open-source model for generating video from text prompts, called Wan2.2-T2V-A14B. This release expands the team's portfolio of powerful, openly accessible foundation models.

What sets this model apart is its use of a Mixture-of-Experts (MoE) architecture. It features 14 billion active parameters, meaning only a fraction of the model's total size is engaged for any given task. This design aims to deliver the performance of a much larger model while keeping the computational cost of inference more manageable.

The release of Wan2.2 is a notable event in the competitive landscape of generative video. By making the model available under the permissive Apache 2.0 license, the Qwen team provides researchers and developers with a powerful, unrestricted tool for building new applications and pushing the boundaries of video synthesis.

The model is now available for download and experimentation on the Hugging Face Hub, allowing the community to begin exploring its capabilities immediately.

Sources

  • Wan-AI/Wan2.2-T2V-A14B

    Hugging Face

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

Get the model

Hugging Face

Specs

Parameters14B · MoE
Resolution480P & 720P
Max duration5s
LicenseAPACHE-2.0
Downloads3.6K
Likes595

Can you run it?

Runs on a laptop — about 15 GB at FP8.

  • BF16 (as published)

    32 GB GPU (RTX 5090) · 48 GB Mac

    29 GB
  • FP8

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

    15 GB
Your machine
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • Precision not published; assuming 16-bit (BF16) weights.
  • Parameter count is an editorial estimate — no weights metadata on Hugging Face.
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Estimated from the parameter count. 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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