Researchers have introduced MoE-ViE, a series of Mixture-of-Experts Vision Encoders designed to scale vision-language model capacity without the proportional increase in computational cost associated with dense architectures. By systematically investigating MoE design spaces for CLIP-style encoders, the authors determined that fine-grained MoE topologies provide superior performance compared to standard MoE or dense models. To optimize the architecture, they implemented an auxiliary-loss-free balancing mechanism to improve expert utilization and developed a specialized MoE kernel to reduce inference latency overhead. To bridge the gap between static images and temporal video data, the framework utilizes frame-level distillation combined with a novel freezing mechanism to preserve existing image knowledge. Empirical results demonstrate that the largest MoE-ViE model achieves zero-shot performance equivalent to a state-of-the-art encoder 1.7x its size, while operating at only 76% of the latency. Furthermore, when integrated with a Large Language Model (LLM), MoE-ViE outperforms existing encoders across various image and video benchmarks, even when compared to models featuring up to 5x more activated parameters.