Joerg Hiller
Jul 27, 2026 01:53
NVIDIA’s Nemotron 3 Ultra achieves 100% accuracy on CVDP benchmarks, revolutionizing RTL coding with agentic workflows and unmatched efficiency.
NVIDIA’s Nemotron 3 Ultra, the flagship model in its Nemotron 3 series, has achieved a 100% pass rate on key tasks within the Comprehensive Verilog Design Problems (CVDP) benchmark. This feat underscores its dominance in register transfer level (RTL) coding workflows, a critical area for modern chip design. The model’s combination of accuracy and efficiency positions it as a game-changer for engineering teams navigating increasingly complex hardware challenges.
Released on June 4, 2026, Nemotron 3 Ultra is a 550-billion-parameter Mixture-of-Experts (MoE) model built for long-running, agentic AI tasks. Unlike prior models, it leverages a Hybrid Mamba-Attention architecture optimized for long-context reasoning, enabling it to handle the iterative nature of RTL coding. According to NVIDIA, the model achieves up to 5.9x higher inference throughput compared to competing models under specific token settings, all while maintaining unparalleled accuracy.
Revolutionizing RTL Workflows
RTL coding demands more than simple code generation—it requires iterative workflows involving debugging, simulation feedback, and precise timing adjustments. Nemotron 3 Ultra, integrated with the ACE-RTL agent, excels in these environments. ACE-RTL employs a generate-test-reflect methodology, where the model iteratively corrects errors based on tool feedback. This approach mirrors real-world engineering practices, significantly improving productivity.
On the CVDP benchmark, tasks range from RTL code completion to debugging and testbench generation. Nemotron 3 Ultra not only achieved a 100% pass rate in debugging tasks but also outperformed other open models across nine categories with a 97.1% average pass rate. For comparison, Kimi K2.6 scored 95.2%, and GLM 5.2 trailed at 92.1%.
Efficiency is another standout feature. Nemotron 3 Ultra uses 28% fewer tokens than GLM 5.2 and 71% fewer than Kimi K2.6, making it significantly more cost-effective for long-running workflows. This efficiency enables engineers to tackle more design iterations within the same compute budget.
Industry Adoption and Ecosystem Integration
Nemotron 3 Ultra is already gaining traction with major EDA (Electronic Design Automation) players. Cadence integrates the model with its ChipStack AI SuperAgent, streamlining RTL verification and debugging. Siemens has paired it with the Questa One Agentic Toolkit to enhance early issue detection and engineering productivity. Similarly, Synopsys’s AgentEngineer automates verification workflows, reducing what used to take weeks into hours.
These integrations highlight the model’s role in transforming chip design, offering companies a competitive edge as they reduce time-to-market and engineering overhead.
Market Implications
NVIDIA’s focus on agentic AI aligns with broader industry trends. With the semiconductor market projected to reach $1 trillion by 2030, according to McKinsey & Company, tools like Nemotron 3 Ultra are crucial for meeting the demand for faster, more efficient chip design processes. NVIDIA’s stock, trading at $206.84 as of July 24, 2026, reflects robust investor confidence, bolstered by its continued dominance in AI-driven solutions.
For developers and enterprises, Nemotron 3 Ultra offers an open model available via platforms like Hugging Face and NVIDIA’s own developer ecosystem. Its ability to reduce costs, improve accuracy, and accelerate workflows makes it an invaluable tool for engineering teams tackling complex hardware problems.
Looking Ahead
Nemotron 3 Ultra’s performance on the CVDP benchmark solidifies its position as the go-to model for RTL coding tasks. With ongoing integration into industry-standard tools and a growing ecosystem, NVIDIA is setting a new benchmark for AI-driven engineering solutions. Developers can start building with Nemotron 3 Ultra today, leveraging its efficiency to stay ahead in the rapidly evolving chip design sector.
Image source: Shutterstock
