Cadence Design Systems on Monday launched Cadence Maestro, a portfolio of artificial intelligence models tuned specifically to help design computer chips, deepening its work with Nvidia and giving chipmakers an option they can run entirely inside their own data centers.
Chip design is a painstaking process that involves arranging billions of transistors precisely to meet cost and performance targets. Cadence and its largest rival, Synopsys, both make the software used for that work, and both are now racing to put AI agents to work on behalf of human engineers to speed up the process.
Maestro was built in part on Nvidia's open-weight Nemotron model and is secured with Nvidia's OpenShell safety technology, the companies said. Cadence said customers can run the models on its own cloud, on any public cloud, or keep them entirely in-house behind their own firewalls and data centers — a key distinction for chip firms that guard their designs as core intellectual property.
Customers can also fine-tune the models on their own chip designs. Cadence said early results show up to 30 percent higher quality and up to five times better cost-efficiency than general-purpose models.
The approach contrasts sharply with Synopsys. On September 30, Synopsys said it would develop an AI model with OpenAI and share revenue with the ChatGPT maker. That model, called GPT-Synopsys, will run on OpenAI-hosted infrastructure, rather than inside a customer's own facilities.
Paul Cunningham, a Cadence senior vice president, framed Maestro as model-agnostic, noting that there are more AI model providers out there than there are chip design software vendors. Asked whether Cadence might strike its own deals with proprietary AI labs, Cunningham said both open- and closed-weight approaches were being pursued equally, and that working with commercial labs was part of the Maestro vision, though he declined to elaborate.
The first Maestro models will reach select early-access customers in the fourth quarter, Cadence said. Shares of Cadence rose 3.9 percent after Monday's announcement. The company was also due to report earnings after the market close on Monday.
For enterprise IT and semiconductor teams, the launch underlines a broader shift: AI tools for engineering are no longer only about capability, but about where the models run, whose infrastructure they depend on, and whether sensitive design data ever has to leave the building.