Building intuition about LLM parameter counts (www.gilesthomas.com)

A new tool has been announced that helps developers intuitively understand parameter counts in large language models (LLMs). Created using the "Sol" variant of OpenAI's recently released GPT 5.6 within the Codex framework, this visualization tool breaks down parameter distribution across different components of GPT-2 models, such as embeddings, attention layers, feed-forward networks (FFNs), and output heads. Users can customize settings, including weight tying and QKV bias, to see how these adjustments impact parameter totals. This innovation holds significance for the AI/ML community as it illuminates the often-overlooked contributions of token embeddings and output heads, particularly in smaller models where these components can comprise a substantial portion of the total parameter count. By clarifying the architecture of LLMs, the tool can assist researchers and developers in optimizing model design and resource allocation, ultimately fostering more efficient training and deployment of AI applications. The emphasis on this breakdown enables a deeper understanding of model efficiency, particularly as vocabulary sizes grow in modern models.