Building intuition about LLM parameter counts

The author shares personal insights from building a GPT-2 implementation in JAX, highlighting the surprising parameter distribution primarily between token embeddings and the output head. They explain the importance of understanding LLM internals and showcase a new visualization tool from OpenAI's GPT 5.6 that helps analyze parameter breakdowns. This post is aimed at developers and those interested in machine learning architecture, particularly regarding LLMs and their component counts.