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This article provides an in-depth interpretation of NVIDIA's newly released 'AI Model Co-Design' paper, pointing out that in AI inference scenarios, storage (memory bandwidth, weight reading) has replaced GPU compute as the primary bottleneck. It elaborates on the design strategies of TensorRT-LLM and Blackwell architecture around the Roofline model, emphasizing that reducing data movement is more critical than improving compute power.