NVIDIA: Vera Rubin Platform Trains the Largest AI Models with a Quarter of Blackwell-Generation GPUs Thanks to Codesign

How many GPUs does the Vera Rubin platform save?

NVIDIA claims the Vera Rubin platform trains the largest AI models using only a quarter of the number of GPUs required on the previous Blackwell generation. The savings come from end-to-end codesign, an approach in which hardware and software are developed together from the start instead of separately. The platform combines Vera CPU processors and Rubin GPUs into a unified design, so optimizations are carried out at the level of the entire system, not just the individual chip.

Technical background

Nemotron 3 Ultra is NVIDIA’s MoE (Mixture of Experts) model with 550 billion parameters, an architecture in which only part of the specialized “experts” within the network is activated for each query, rather than the whole model. The model achieved a score of 71.7 percent on SWE-bench Verified, a benchmark that measures the ability to solve real bugs reported on GitHub.

Test results

Prime Intellect tested Vera CPU processors in RL (reinforcement learning) sandbox environments and measured about 30 percent higher throughput per processor compared to alternative x86 architectures. NVIDIA presents post-training, the continuous refinement of a model after initial training through additional data and feedback, as an ongoing process rather than a one-time step. The result shows that the choice of processor architecture directly affects the speed of the RL sandbox cycle, not just the raw number of GPUs available in the system.

What this means for the market

NVIDIA highlights “intelligence per dollar” as the key metric for agentic AI, rather than raw computing power alone. If the claim of a quarter of Blackwell-generation GPUs holds up in practice, the cost of training a model of comparable size could be significantly reduced compared to the previous generation. Instead of comparing generations solely by transistor count or raw FLOPs, NVIDIA proposes measuring progress by the ratio of a model’s achieved intelligence to the money invested, which directly determines how many GPUs companies must buy to train comparable models.