analiza aiqbench

The surge in AI inference workloads is driving a fundamental shift away from traditional compute-centric designs toward architectures that optimize memory bandwidth and storage efficiency. This transition highlights the critical bottleneck of moving massive model weights between chips and memory, necessitating new hardware approaches like HBM3e and specialized interconnects to sustain performance gains. Consequently, the industry must prioritize co-design strategies that align chip topology with the specific data movement patterns of large-scale inference models.