For auto regressive LLMs in particular, the result of a forward pass is a single next token prediction. In practice, model weights are copied in smaller chunks than entire layers, but layers are just easier to visualize. The important takeaway is that for every single token generated, the entire model, all billions of parameters, must complete this journey from the HBM to the processor. Throughout the video, I'll refer to this journey as a "model stream" or "model copy". Just remember, it's not a one-time atomic transfer. It's a continuous pipeline of bytes being copied.