Wan2.2-S2V-14B Model Architecture Analysis and Computational Optimization
Wan2.2-S2V-14B employs a hybrid expert (MoE) architecture as its core technology solution. The architecture decomposes the 27B total-parameter model into multiple expert modules, and activates only the 14B parameters during the inference process, which is achieved by dynamically selecting the most relevant expert sub-networks through a gating mechanism.The MoE architecture has two key advantages over the traditional dense models: firstly, it reduces the amount of real-time computation of more than 70% by the parameter-sharing mechanism, and secondly, it maintains the full-parameter model's expressive power. In practice, this architecture allows the model to run on a single GPU server equipped with 80GB VRAM without the need for large-scale compute cluster support. the Wan-AI team specially designed the parameter offloading mechanism (offload_model), which can temporarily store part of the model components in CPU memory, further reducing the graphic memory requirement.
This answer comes from the articleWan2.2-S2V-14B: Video Generation Model for Speech-Driven Character Mouth SynchronizationThe
