NVIDIA explains how full-stack collaborative design breaks AI memory bottlenecks, boosting agent throughput by up to 30 times per dollar.
NVIDIA's proposal includes compressing weights and KV cache using NVFP4, distributing weights across up to 72 GPUs, using LPDDR memory on Vera CPUs for local buffering, and moving inactive contexts to CMX storage via Dynamo.
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