Key Takeaways
- ACE-RTL agent with Nemotron 3 Ultra achieves 97.1% average pass rate across 9 RTL task categories, surpassing GLM 5.2 and Kimi K2.6.
- Token usage per iteration is up to 71% lower than competitors, enabling more efficient agentic workflows.
- Nemotron 3 Ultra’s hybrid Mamba-Attention MoE architecture and 1M context length are tailored for long-running, iterative RTL coding agents.
How NVIDIA’s Nemotron 3 Ultra Reshapes AI-Driven Chip Design
NVIDIA’s Nemotron 3 Ultra has emerged as the top-performing open model for agentic RTL coding, achieving a 97.1 percent average pass rate on the comprehensive Verilog design problems (CVDP) benchmark. This represents a significant leap in accuracy for AI-assisted hardware design, surpassing leading models from Chinese labs GLM and Kimi while cutting per-iteration token consumption by as much as 71 percent.
Table of Contents
Breaking Down the ACE-RTL Agent and CVDP Performance
ACE-RTL, or agentic context evolution for RTL, is a multi-step workflow designed to mimic how human engineers debug hardware designs. It consists of a generator, a reflector, and a coordinator that iteratively refine RTL code based on simulation feedback. This process mirrors the real-world cycle of write, test, and fix.
On the CVDP benchmark, which includes nine categories such as code completion, specification to RTL generation, debugging, and testbench creation, ACE-RTL paired with Nemotron 3 Ultra achieved an average pass rate of 97.1 percent. This outperforms GLM 5.2 at 92.1 percent and Kimi K2.6 at 95.2 percent. Notably, Nemotron 3 Ultra reached a perfect 100 percent pass rate on several categories.
The Token Efficiency Advantage
Nemotron 3 Ultra uses on average 6,629 tokens per iteration, compared to 9,156 for GLM 5.2 and 22,579 for Kimi K2.6. This translates to 28 to 71 percent fewer tokens, directly reducing inference costs and latency, which is critical for iterative agentic loops.
Architecture Built for Agentic Workloads
The model employs a hybrid Mamba-Attention mixture-of-experts (MoE) architecture with 550 billion total parameters and 55 billion active. It supports a context window of up to 1 million tokens, necessary for managing the growing context in debugging sessions. NVIDIA reports up to 5x higher throughput and 30 percent lower cost compared to other open models, a claim that has not yet been independently benchmarked at production scale but is supported by company-published data on the Hugging Face model card.
Strategic Implications for the AI Hardware Industry
Nemotron 3 Ultra’s performance on agentic RTL tasks signals a shift in how AI models can be applied to hardware design. The combination of high accuracy and low token consumption makes it a practical foundation for EDA integration. Partners including Cadence, Siemens, and Synopsys have already announced integrations, indicating strong industry buy-in.
From a competitive perspective, Nemotron 3 Ultra undercuts larger closed models on cost efficiency. Independent research from Unsloth indicates the model can run on as few as 8 B200 or 16 H100 GPUs, with quantization options yielding minimal accuracy loss. This lowers the barrier to entry for hardware teams exploring AI-assisted design.
Further, experiments from NVIDIA’s developer blog show that agent harness optimization can boost Nemotron 3 Ultra’s benchmark scores without fine-tuning. For example, adding a ReadFileContinuationNoticeMiddleware raised a LangChain Deep Agents evaluation score from 94 to 96 out of 127. Such techniques may compound the model’s existing advantages in production settings.
Looking Ahead: Open Models in Hardware Design
Nemotron 3 Ultra establishes a new benchmark for open models in the specialized domain of RTL coding. Its success highlights the effectiveness of task-specific synthetic data pipelines and agentic workflows. As hardware design grows more complex, AI agents that can iterate efficiently will become indispensable. NVIDIA’s open approach allows the broader community to build on these advances.
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Frequently Asked Questions
What is ACE-RTL and how does it work?
ACE-RTL stands for agentic context evolution for RTL. It is a multi-step workflow that mimics human debugging of hardware designs, consisting of a generator, a reflector, and a coordinator that iteratively refine RTL code based on simulation feedback.
How does Nemotron 3 Ultra perform on the CVDP benchmark?
Nemotron 3 Ultra achieved a 97.1% average pass rate on the CVDP benchmark, outperforming GLM 5.2 (92.1%) and Kimi K2.6 (95.2%), with a perfect 100% pass rate on several categories.
What is the token efficiency advantage of Nemotron 3 Ultra?
Nemotron 3 Ultra uses on average 6,629 tokens per iteration, which is 28-71% fewer tokens compared to competitors like GLM 5.2 (9,156 tokens) and Kimi K2.6 (22,579 tokens), reducing inference costs and latency.
What is the architecture of Nemotron 3 Ultra?
It uses a hybrid Mamba-Attention mixture-of-experts (MoE) architecture with 550 billion total parameters, 55 billion active, and supports up to 1 million token context windows.
What are the strategic implications of Nemotron 3 Ultra for the hardware design industry?
Nemotron 3 Ultra’s high accuracy and low token consumption make it practical for EDA integration. Partners like Cadence, Siemens, and Synopsys have already announced integrations, lowering the barrier to entry for AI-assisted hardware design.
Can Nemotron 3 Ultra be run on limited hardware?
Yes, independent research indicates it can run on as few as 8 B200 or 16 H100 GPUs, and quantization options yield minimal accuracy loss.
What makes Nemotron 3 Ultra suitable for agentic workflows?
Its high throughput (up to 5x) and low cost (30% lower than other open models), combined with a large context window and efficient iterative refinement, make it ideal for agentic RTL coding and debugging loops.
