The AI cloud market is experiencing exceptionally rapid growth worldwide, with the latest reports projecting annual growth rates between 28% and 40% over the next five years. It may reach up to $647 billion by 2030, according to various analyst reports. The surge in AI cloud adoption, GPU-as-a-service platforms, and enterprise interest in AI “factories” has created new pressures and opportunities for product engineering and IT leaders. Regardless of which public cloud or private cluster you choose, one key differentiator sets each AI and HPC solution apart: the performance of storage.
While leading clouds often use the same GPUs and servers, the way data flows — between compute, network, storage, and persistent layers —determines everything from training speed to scalability. Understanding storage fundamentals will help you architect or select the right solution. We have previously covered how to build AI cloud solutions, and with hands-on experience in this space, we would like to share our thoughts in this article.