NVIDIA notes that the rapid proliferation of large-model applications has sharply intensified GPU memory pressure, HBM supply is tightening, and prices are poised to keep climbing. In response, the company has introduced a full-stack co-design approach—optimizing GPU, CPU, networking, storage, and software as an integrated whole. This approach breaks the AI memory bottleneck through several techniques: compressing data into low-precision formats, distributing model weights across GPUs, using CPU memory to buffer context, migrating inactive context via inference software, and offloading RAG indexes to the CPU and storage tiers. Tests show that the new platform, despite only a modest increase in HBM capacity, delivers a substantial boost in agent throughput compared with the previous-generation platform. The key battleground in AI computing has now shifted to how many effective tokens can be produced per byte and per watt.