In the past, everyone was rushing 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 offloading critical inference tasks to local data centers, which not only reduces latency but also saves bandwidth and cloud rental costs.

Competing for memory is a typical feature in the early stages of deep learning training (whoever has the larger video memory wins), but today:

The throughput limit of data stored to 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 computing power deployment scale.

If the data center layout is still based on the design paradigm of traditional web/database business from 2015, it will directly fail under AI workloads.

Visit Forbes to see our insights: https://www.forbes.com/councils/forbestechcouncil/2025/08/08/20-tech-experts-on-emerging-hardware-trends-businesses-must-watch/