NVIDIA Vera Rubin: Maximum Intelligence per Dollar for Agentic AI

NVIDIA explains in a blog that for agentic AI, post-training is no longer a one-time phase but a continuous process. Unlike generative models, agentic models must plan, use tools, and adapt to changing conditions. Post-training includes cycles of forward and backward passes where the model improves its skills through reinforcement learning (RL). The key metric becomes "intelligence per dollar," which combines the cost per token during inference with training efficiency. The NVIDIA Vera Rubin platform, successor to Blackwell, is designed for this scenario: it allows training the largest models with four times fewer GPUs. As an example, the Nemotron 3 Ultra model with 550 billion parameters (Mixture of Experts architecture) achieved 71.7% on SWE-bench verified. The Vera Rubin platform integrates with NeMo libraries (NeMo Gym, NeMo RL) and is optimized for parallel deployments of thousands of environments. Partners include Prime Intellect (30% throughput increase on Vera processors compared to x86), Perplexity (RL stack with RDMA weight synchronization in under 2 seconds), and Together AI (post-training as a service on the AI Native Cloud platform).