AI Compute Landscape: GPUs, Networking, and Storage

ChatGPT’s launch is likened to the birth of Windows, positioning large language models as a new entry point for information systems and potentially reshaping the software ecosystem.

AI compute relies on three pillars: Computation , Networking , and Storage . GPUs are the central engine for training and inference, with rapid performance gains outpacing Moore’s Law, driving a booming AI server market. High‑speed networking, such as NVIDIA’s NVLink and InfiniBand, is becoming the primary bottleneck, prompting the development of 800 G‑ to 1.6 T optical modules. Storage faces the “memory wall”; advances in NAND, DRAM, and 3D stacking, along with high‑bandwidth HBM, are critical for future GPU performance.

The analysis is excerpted from the research report AI算力研究框架（2023） , which examines compute chips, technologies, networks, and storage in depth.

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