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).

According to @godofprompt, MIT researchers demonstrated that up to 90% of a neural network’s parameters can be deleted without losing model accuracy, a finding known as the 'Lottery Ticket Hypothesis' (source: MIT, 2019). Despite this, the technique has rarely been implemented in production AI systems over the past five years. However, growing demand for cost-effective and scalable AI solutions is now making network pruning a production necessity, with the potential to reduce inference costs by up to 10x (source: Twitter/@godofprompt, 2026). Practical applications include deploying more efficient AI models on edge devices and in enterprise settings, unlocking significant business opportunities for companies seeking to optimize AI infrastructure spending.