AI Chips
How training and inference silicon, accelerators and on-device NPUs get chosen, and how capacity, supply and export rules shape delivery timelines.
AI chips decide how fast a model runs and what each run costs. Training clusters depend on interconnect bandwidth and memory capacity, inference clusters on per-card throughput and power draw, and on-device setups on unified memory and thermal limits. What matters is tracking measured results, capacity shifts and export rules, and answering a practical question: which card fits which workload, and which machine fits which budget.
