Nvidia recently announced it is leveraging its own Vera central processing units (CPUs) to run critical chip design software, significantly speeding up the development of next-generation graphics processing units (GPUs). This move involves close collaboration with Cadence Systems and Synopsys, the leading providers of electronic design automation (EDA) software, which have optimized their platforms to take advantage of Vera CPUs’ performance benefits.

The improvements are particularly notable in Cadence’s Jasper formal verification tool and Synopsys’ VCS logical simulation software, both essential in validating semiconductor designs before fabrication. Nvidia reported these applications achieved a 1.5-fold performance increase when executed on Vera CPUs compared to previous setups.

Beyond hardware enhancements, Nvidia is integrating its PhysicsNeMo libraries—AI models specialized in physics—and GPU math libraries into its Agent Toolkit. This integration allows autonomous AI agents to utilize accelerated solvers alongside third-party tools, increasing automation throughout chip design phases. This development was highlighted at the 2026 Design Automation Conference in Long Beach, California.

Chip design traditionally involves labor-intensive simulation, verification, and implementation stages, often requiring engineers to run thousands of iterative tests over several years to finalize a design. While graphics processing units (GPUs) and AI have sped up some parts of this work, many EDA tasks depend on fast, single-core CPU performance, efficient memory handling, and sound throughput—characteristics fundamental to the Vera CPU architecture.

By tailoring Vera CPUs to these computationally demanding tasks, Nvidia enhances two vital early-stage design processes: formal verification and logical simulation. Cadence’s Jasper uses machine learning and smart proof technologies to detect and correct design errors rapidly, while Synopsys’ VCS simulates the chip’s logical behavior to validate the design's correctness before manufacturing.

Nvidia’s focus on increasing automation through AI agents also reflects a broader industry trend to reduce engineering bottlenecks. These agents can autonomously navigate complex workflows, calling accelerated solvers and other design tools to speed up problem detection and solution generation.