Generative AI Infrastructure: How GPUs, AI ASICs, and Networking Chips Shape Datacenters

Generative AI is fundamentally changing how datacenters are built, putting three types of silicon center-stage: GPUs, custom AI ASICs, and advanced networking processors. Driven by these technologies, the datacenter processor market soared to $147 billion in 2024 and is expected to double by 2030, largely thanks to explosive growth in GPUs and specialized AI ASICs.

While GPUs remain the reference for AI training and inference, hyperscale providers, eager to reduce their dependence on Nvidia, are increasingly co-designing specialized AI ASICs with chipmakers like Broadcom, Marvell, and Alchip. These ASICs sacrifice some versatility to achieve superior performance and energy efficiency, creating opportunities for a thriving startup scene featuring companies like Groq, Cerebras, and Tenstorrent, and spurring major waves of venture investment and mergers. Crucially, chiplet architectures, which combine multiple smaller chip components into a single, optimized package, are now key to driving GPU and ASIC performance upward, beyond what traditional single-chip designs can deliver.

As AI models become ever larger and require responses within milliseconds, networking silicon has become just as critical as processors themselves. DPUs, smart network cards, and advanced switches now coordinate massive arrays of accelerators, making both scale-up and scale-out networks a pivotal part of datacenter performance.