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

**[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 8 min read · 0 upvotes · 0 comments

## Summary

NVIDIA Nemotron 3 Ultra, a 550B-parameter Mixture-of-Experts Hybrid Mamba-Attention model, achieves leading performance on agentic RTL (Register Transfer Level) coding tasks when paired with the ACE-RTL agent framework. Using the CVDP benchmark across nine hardware design task categories, Nemotron 3 Ultra reaches a 97.1% average pass rate — outperforming GLM 5.2 (92.1%) and Kimi K2.6 (95.2%) — while using 71% fewer tokens than Kimi K2.6. The ACE-RTL agent uses a generate-test-reflect loop where a generator, reflector, and coordinator collaborate to iteratively fix RTL errors using simulation feedback. Nemotron 3 Ultra was trained on a synthetic data pipeline covering spec-to-RTL generation, code editing, and debugging tasks, including injected realistic hardware bugs. The model is available as open weights and integrates with EDA partners including Cadence, Siemens, and Synopsys.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-leads-open-models-on-accuracy-and-efficiency-in-agentic-rtl-coding>

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