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NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding
NVIDIA Developer · Published · 3 min read
In 30 seconds
- What happened
- NVIDIA released Nemotron 3 Ultra, achieving 97.1% pass rate on RTL coding tasks, outperforming competitors while using 71% fewer tokens.
- Why it matters
- Hardware engineers and chip designers using AI agents for register transfer level code generation, modification, debugging, and verification workflows.
- Watch out
- Results measured on CVDP benchmark with ACE-RTL agent framework; real-world performance on production designs and integration with EDA tools requires validation.
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From the article
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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AI-Generated Summary
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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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