There is a common misconception that one GPU cloud is very much like another. But that is simply not the case. They are built with different technologies and architectures, and they come with their own sets of challenges, advantages, and disadvantages.
The most advanced AI cloud operators today are developing new blueprints for GPU datacenters that deploy NVIDIA H100s in Kubernetes or other virtualized environments to reach new levels of performance for AI processing. To the customer the specs are basically the same. AI cloud computing service providers boast about Nvidia HGX H100 arrays and the fact they have 3.
2 terabytes of InfiniBand. But this is because they’re all using the same network cards. If all clouds seem the same from a technical standpoint, customers will make decisions based on price.
But technical specifications alone do not tell the whole story. You can buy a Toyota Corolla with 100 kilowatts of power, and you buy a Mercedes with 100 kilowatts of power, but they are not the same thing. The build quality is different, the cost is different, and the user experience is different.
It is the same with data centers. If the head of finance oversaw architecting, it’s likely we would have the Toyota Corolla of data centers, and that’s fine for some, but given the choice, most organizations are going to pick the Mercedes. A data center built with cost savings at its heart might work for some customers, but it will be slower, and/or offer less cloud storage , and.