How Native Multimodal AI Kills Lag

This research examines the development and scaling laws of Native Multimodal Models (NMMs), which are AI systems trained from scratch to process both images and text simultaneously. The sources compare early-fusion architectures, which integrate raw multimodal signals from the start, against traditional late-fusion models that rely on separate pre-trained encoders. Findings indicate that early-fusion models are more efficient to train, easier to deploy, and perform as well as or better than late-fusion counterparts at lower compute budgets. Furthermore, the study highlights that incorporating a Mixture of Experts (MoE) significantly boosts performance by allowing the model to learn modality-specific weights. This specialized approach enables sparse models to handle heterogeneous data more effectively than dense architectures while maintaining the same inference cost. Ultimately, the reports suggest that NMMs follow predictable scaling properties similar to large language models, providing a blueprint for the next phase of edge AI development.