Building intuition about LLM parameter counts :: Giles' blog
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Building intuition about LLM parameter counts
Posted on 10 July 2026
in
AI
When I was building my GPT-2 implementation in JAX,<br>I started with just token embeddings for the input, and a separate output head (as I was not using<br>weight tying). It wasn't an<br>LLM -- no Transformer blocks, no attention, no feed-forward networks.
I was somewhat surprised when I noticed that even that stripped-down model had 77 million parameters<br>with the "small" settings I was using to train -- specifically, an embedding dimension of 768.<br>However, I realised I shouldn't be -- with a vocab size of 50,257, each of those components is essentially<br>a 768×50,257 matrix, and that is indeed over 38 million numbers.
But the finished LLM at the end of the project was only 163 million parameters -- that meant that the<br>input and output components alone were almost half of it. That felt<br>like a surprisingly large percentage.
I had a similar shock when I was first looking into the feed-forward network,<br>and realised that it had roughly twice as many parameters as the attention layers.
When we learn about the internals of LLMs, a lot of the focus is on the attention<br>mechanism. This makes sense -- it's the hardest part to get your head around. The<br>rest of the setup, at least for simple GPT-2 type models, is fairly standard stuff.
But that means that it is easy to overestimate how much of the total<br>parameter count of the model attention uses up -- especially for smaller models,<br>where the token embeddings and the output head are so large in comparison to<br>the Transformer layers that make up the actual body of the LLM.
OpenAI released GPT 5.6 today, so I decided to take its "Sol" variant for a ride<br>in Codex and asked it to write a visualiser.<br>It shows<br>breakdowns of how the parameters are split between embeddings, attention, the FFNs, and<br>the output head<br>for...