[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 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? submitted by /u/ABHISHEK7846 [link] [comments.