NVIDIA claims Vera Rubin trains a 10T MoE with a quarter the GPUs of Blackwell NVL72.

In a July 17 post, NVIDIA pushes "intelligence per dollar" as the agentic-era metric, arguing post-training rather than pretraining is now the central workload because agents need continuous improvement cycles. The claim: a 10-trillion-parameter MoE on 100 trillion tokens in one month using 25% as many GPUs. Supporting open libraries are NeMo Gym (environments as dataset + agent harness + verifier + per-task state) and NeMo RL. Vendor benchmark, treat accordingly, but the reframing of post-training as the main event tracks what I see people actually spending compute on.