NVIDIA has positioned its upcoming Vera Rubin platform as a foundational infrastructure solution for the emerging era of agentic artificial intelligence, specifically targeting the computational demands of continuous post-training workloads. Unlike traditional generative models that respond to static prompts, agentic AI systems operate dynamically, requiring constant adaptation to shifting environments, tools, and edge cases. This paradigm shift transforms post-training from a one-time optimization phase into a continuous, production-looped process that demands sustained compute resources. Central to this architecture is NVIDIA’s focus on maximizing intelligence per dollar, a metric that evaluates the economic efficiency of model refinement rather than merely tracking inference costs. The company emphasizes that lowering the cost per token directly amplifies the value of every intelligence point embedded during post-training. To achieve this, NVIDIA developed the NeMo open-source ecosystem, which standardizes distributed post-training through libraries like NeMo Gym for training environments and NeMo RL for reinforcement learning orchestration. This infrastructure converts bespoke research workflows into scalable, repeatable operations capable of managing thousands of parallel environments and weight synchronization cycles. Validating this approach, NVIDIA showcased its Nemotron 3 Ultra, a 550-billion-parameter mixture-of-experts model trained using fully disclosed post-training recipes on NeMo RL. The model achieved a 71.7 percent verified score on the SWE-bench real-world coding benchmark, successfully resolving approximately seven out of ten open-source software bugs. The computational feasibility of such intensive training loops is underpinned by NVIDIA’s Blackwell architecture, which reduces per-run costs, while the forthcoming Vera Rubin platform is engineered to train the industry’s largest models using one-fourth the GPU count of the previous generation. Early industry adoption underscores Vera Rubin operational impact. Prime Intellect has integrated the platform’s Vera CPUs with its reinforcement learning sandbox, reporting an average thirty percent throughput increase compared to alternative x86 architectures. Perplexity currently runs asynchronous reinforcement learning stacks across hundreds of GPUs, utilizing RDMA-based synchronization to update trillion-parameter models in under two seconds, while Together AI offers comprehensive post-training services on NVIDIA hardware and plans to leverage Vera Rubin to accelerate its training-to-inference iteration cycles. Together, these implementations highlight a broader industry pivot toward economically viable, continuous model refinement. By aligning hardware architecture with the relentless compute requirements of agentic workflows, NVIDIA aims to establish Vera Rubin as the standard for next-generation AI factories.