Each wave of AI has created a new scaling law. Pretraining scaled intelligence through larger datasets, more parameters, and massively parallel GPU systems. Post-training scaled usefulness through instruction tuning, and re-balancing GPUs for generative inference. Test-time scaling improved reasoning by giving models more generated tokens for thinking.
Now, agentic AI and reinforcement learning scale actions. Models take more steps, call more tools, run more evaluations, and interact with execution environments to perform tasks.
This blog explains how NVIDIA Vera CPUs help AI factories to scale agentic AI and reinforcement learning by shortening CPU execution time, increasing task throughput, improving overall AI factory output, and enabling smarter, longer-thinking agents.
Figure 1. CPU execution becomes part of the AI loop
Why CPUs matter more in the agentic era
