[P] Visualizing token-level activity in a transformer

I’ve been experimenting with a 3D visualization of LLM inference where nodes represent components like attention layers, FFN, KV cache, etc.

As tokens are generated, activation paths animate across a network (kind of like lightning chains), and node intensity reflects activity.

The goal is to make the inference process feel more intuitive, but I’m not sure how accurate/useful this abstraction is.

Curious what people here think — does this kind of visualization help build intuition, or does it oversimplify what’s actually happening?