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

Agentic AI / Generative AI

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

Jul 26, 2026

By Nirmal Kumar Juluru , Chenhui Deng , Cunxi Yu , Nathaniel Pinckney and Brucek Khailany

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The ACE-RTL agent, in combination with NVIDIA Nemotron 3 Ultra, delivers state-of-the-art accuracy and efficiency in agentic RTL workflows by leveraging an iterative generate-test-reflect approach and long-context, RTL-specialized reasoning.

On the comprehensive Verilog design problems (CVDP) benchmark, ACE-RTL with Nemotron 3 Ultra achieves a 97.1% average pass rate across nine agentic RTL task categories, outperforming models like GLM 5.2 and Kimi K2.6 while using up to 71% fewer tokens per iteration.

Nemotron 3 Ultras hybrid Mamba-Attention Mixture-of-Experts architecture and training on a diverse, rubric-filtered, synthetic RTL dataset enable high-throughput, low-cost, long-context inference, making it a practical foundation for integration with leading EDA tools from Cadence, Siemens, and Synopsys.

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Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware knowledge, precise reasoning, a

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