According to God of Prompt on Twitter, the evolution of Mixture of Experts (MoE) models is creating significant advantages for the open source AI community compared to dense models. Dense models like Meta's Llama 405B require retraining the entire model for any update, resulting in high costs—over $50 million for Llama 405B (source: God of Prompt, Jan 3, 2026). In contrast, DeepSeek's V3 MoE model achieved better results with a lower training cost of $5.6 million and offers modularity, allowing for independent fine-tuning and capability upgrades. For AI businesses and developers, MoE architectures present a scalable, cost-effective approach that supports rapid innovation and targeted enhancements, widening the gap between dense and modular AI models for open-source development.

According to God of Prompt, adopting modular and composable AI model architectures can reduce training and inference costs by 5-10x, enable faster iteration cycles, and provide flexibility for enterprise AI development. However, this approach introduces complexities, such as the need for correct implementation, load balancing during training, and higher memory overhead since all experts must fit in VRAM. For most business cases, the cost and speed benefits outweigh the challenges, making this an attractive strategy for AI teams focused on scalability and rapid deployment (Source: God of Prompt, Twitter, Jan 3, 2026).