Agentic AI · Blackwell · Nvidia · NVIDIA Blog
Prime Intellect’s Lab continuously post-tunes frontier open models on NVIDIA Blackwell and taps NVIDIA Dynamo for inference
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Prime Intellect has optimized its sandbox infrastructure to integrate with NVIDIA Vera CPUs, enabling low-latency, energy-efficient reinforcement learning.
Key facts
- NVIDIA Nemotron 3 Ultra, an open weight, 550-billion-parameter mixture-of-experts (MoE) model, offers verifiable benchmarks and a fully disclosed post-training recipe run on NeMo RL
- When comparing realistic RL sandbox workloads against alternative x86 architectures, Prime Intellect found that Vera delivers, on average, 30% greater throughput per CPU
- Prime Intellect’s Lab continuously post-trains frontier open models on NVIDIA Blackwell and uses NVIDIA Dynamo for inference orchestration
- Cost per token is the key metric for the inference factory: the all-in cost of delivering 1 million tokens
Summary
Agentic AI works the same way. That’s why post-training, the phase that refines a model after initial training on raw data, is no longer a one-time finishing step. It’s continuous, because the environment that agentic models operate in shifts fast. Post-training runs loop back from production as new problems surface. The goal of post-training is to maximize intelligence per dollar by maximizing the yield of every forward and backward pass in the continuous learning cycle. Post-training is where intelligence is built.