What happens when a GPU reads memory

The thread was overwhelmingly about GPU memory architecture as a learning exercise rather than AI itself, so the AI-specific discussion stayed narrow. The one substantive AI tangent asked whether increasingly capable automated kernel tuning changes the old hardware-design tradeoff: instead of exposing every complexity in silicon, vendors might build simpler accelerators and let optimization software discover efficient implementations. That possibility was treated as plausible rather than established, with an explicit concern that dedicated tensor or neural processors can gain simplicity only by becoming too narrow for broader workloads. Several readers also brushed against a practical issue for AI systems work: understanding and tuning modern GPU behavior is difficult when critical implementation details are undocumented. One response suggested that competing hardware documentation could provide a clearer conceptual route than reverse-engineering opaque NVIDIA behavior. But nobody supplied evidence that AI-driven tuning has already made simpler GPU designs broadly successful, nor did the discussion compare model throughput, training costs, or real-world accelerator performance. The dominant mood was technical curiosity: readers appreciated material difficult enough to expose how much they still had to learn, while recognizing that the subject remains inaccessible without deeper computer-architecture grounding.