In the past, everyone was desperately moving to the cloud, but the cost of computing power during the inference phase made many teams realize: long-cycle, large-scale AI inference burns money too quickly in the cloud. AI-native applications are more suitable for pushing key inference tasks down to local data centers, which reduces latency and saves bandwidth and cloud rental costs. Memory contention is a typical feature in the early stages of deep learning training (whoever has the larger GPU memory wins), but today: The data throughput limit stored on the GPU directly affects inference QPS. The interaction speed between GPU and CPU/acceleration cards is the upper limit of pipeline performance. The power consumption of a single rack AI cluster can reach several tens of kilowatts, and unreasonable PD design can directly bottleneck the scale of computing power deployment. If the data center layout is still stuck in the design paradigm of traditional Web/database business from 2015, it will directly crash under AI workloads. Check out our insights: 20 Tech Experts On Emerging Hardware Trends Businesses Must Watch via @forbes