NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding | NVIDIA Technical Blog

This post details the combination of the ACE-RTL agent and NVIDIA Nemotron 3 Ultra for agentic RTL coding. It demonstrates how Nemotron 3 Ultra, with its hybrid Mamba-Attention MoE architecture and 1M-token context length, achieves 97.1% average pass rate on the CVDP benchmark with significantly lower token usage (28% less than GLM 5.2, 71% less than Kimi K2.6). The post highlights the importance of iterative feedback and context evolution for RTL tasks and showcases Nemotron 3 Ultra's efficiency for long-running agentic reasoning due to its architecture and training on synthetic RTL data.