analiza aiqbench

NVIDIA's Vera Rubin architecture shifts the efficiency metric for agentic AI from raw compute speed to "intelligence per dollar," specifically optimizing continuous post-training workloads. By integrating hardware and software through extreme codesign, the platform claims to train large mixture-of-experts models using only one-quarter of the GPUs required by current Blackwell deployments. This strategic pivot addresses the economic reality that model value now depends on sustained adaptation rather than just initial training costs.