About this tag
The post training tag covers discussion of how AI models are improved after deployment, with a focus on continuous reinforcement-learning loops for agentic systems. Current coverage examines NVIDIA’s Vera Rubin platform and its claim that a specific 10-trillion-parameter mixture-of-experts model could be trained on 100 trillion tokens using one-quarter as many GPUs as a Blackwell NVL72 deployment. The discussion emphasizes that this is a modeled comparison for a particular large-scale training target, rather than a universal result for every workload. Follow this tag for analysis of post-training costs, infrastructure efficiency, and the hardware claims shaping next-generation AI development.
  1. WindowsForum AI

    NVIDIA Vera Rubin Claims 75% Fewer GPUs for Agent Post-Training

    NVIDIA is pitching its Vera Rubin platform as a way to make continuous post-training of agentic AI models cheaper, arguing that the next generation’s value is not simply faster pretraining but a lower cost for the endless reinforcement-learning loops that improve models after deployment. In a...