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

[NVIDIA Developer Blog](https://yomu.fyi/company/nvidia-developer-blog) · Nirmal Kumar Juluru · Jul 27, 2026

**Type:** Benchmark

## Summary

Register transfer level (RTL) development relies heavily on iterative verification and tool feedback, which poses challenges for standard code generation models. To address realistic hardware design tasks, the ACE-RTL agent pairs an iterative generate-test-reflect workflow with NVIDIA Nemotron 3 Ultra. The model utilizes a hybrid Mamba-Attention Mixture-of-Experts architecture trained on a synthetic dataset encompassing code generation, editing, and error debugging. Across the comprehensive Verilog design problems benchmark, ACE-RTL with Nemotron 3 Ultra attained a 97.1% average pass rate, outperforming GLM 5.2 and Kimi K2.6. Additionally, Nemotron 3 Ultra achieved its accuracy while consuming an average of 6,629 tokens per iteration, which is substantially lower than competing models.

## Context

Modern chip design faces bottlenecks because RTL development and verification require specialized hardware knowledge, precise temporal reasoning, and repeated interaction with EDA tools to resolve bugs exposed during tool-based validation.

## Approach / What changed

NVIDIA combined the ACE-RTL agent (featuring generator, reflector, and coordinator components) with Nemotron 3 Ultra, a 550B total-parameter (55B active) hybrid Mamba-Attention MoE model trained on an RTL synthetic data generation pipeline covering spec-to-RTL, code editing, and debugging tasks.

## Takeaways

- ACE-RTL with Nemotron 3 Ultra achieved a 97.1% average pass rate across nine CVDP task categories, outperforming Kimi K2.6 (95.2%) and GLM 5.2 (92.1%).
- Nemotron 3 Ultra averaged 6,629 tokens per iteration on CVDP tasks, representing roughly 28% fewer tokens than GLM 5.2 (9,156) and 71% fewer than Kimi K2.6 (22,579).
- Nemotron 3 Ultra uses a 550B total, 55B active parameter hybrid Mamba-Attention MoE architecture supporting a 1M-token context length.

**Tags:** [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Performance](https://yomu.fyi/topic/performance), [Testing](https://yomu.fyi/topic/testing)

[Read original post](https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-leads-open-models-on-accuracy-and-efficiency-in-agentic-rtl-coding)