Where model-specific silicon fits

Those tradeoffs significantly narrow the range of enterprise workloads where model-specific silicon is likely to make economic sense.

Dai sees the technology as best suited for mature, predictable inference workloads that run at massive scale and rely on relatively stable AI models, such as customer service automation, fraud detection, industrial computer vision, network operations, edge AI, and embedded copilots.

For CIOs, that effectively limits model-specific silicon to a small subset of enterprise AI deployments, rather than a wholesale replacement for GPU infrastructure, he said. “GPUs will remain the preferred enterprise platform because most enterprises value flexibility, multi-tenancy, and rapid model evolution over maximum efficiency.”