GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE www.semianalysis.com/p/gpt-4…tructure > We have gathered a lot of information on GPT-4 from many sources, and today we want to share. This includes model architecture, training infrastructure, inference infrastructure, parameter count, training dataset composition, token count, layer count, parallelism strategies, multi-modal vision adaptation, the thought process behind different engineering tradeoffs, unique implemented techniques, and how they alleviated some of their biggest bottlenecks related to inference of gigantic models.