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Cadence reveals Maestro, fine-tuned AI models built to run specialized chip design agents, with early access coming for 'select customers' in Q4 2026
Customers can run the models on-premises and post-train them on their own designs and data.
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Cadence, one of the largest electronic design automation (EDA) companies, has unveiled Maestro, its new portfolio of domain-specialized AI models tailored for its chip design agents, in a press release sent just ahead of earnings. In initial results compared with general-purpose AI models, Cadence claims up to a 30% improvement in the quality of results and that Maestro effectively uses fewer tokens for the same task, with up to 5x the token efficiency. The first models “are available to select early access customers” in Q4 2026, and customers may run them on Cadence’s cloud, public clouds, or customer-controlled on-premises environments and post-train them on their own “proprietary data, designs, and workflows.” To learn more about Maestro, we spoke with two Cadence executives: Rob Knoth, senior group director of strategy and new ventures, and Paul Cunningham, senior vice president and general manager.
This approach is distinct from Synopsys’ own specialized model, GPT-Synopsys, which will run on OpenAI-hosted infrastructure, without using customer data to train the model. The models of both EDA giants, announced less than a month apart, now split on where a customer’s design data goes. Where they might not differ is why: Paul Cunningham, senior vice president and general manager at Cadence, told Tom’s Hardware Premium that agent costs have “really started to explode,” adding, “And I think the world realized so.” That makes it a natural step, he said, to move more of the agents’ iterating “into the weights of the models themselves.”
Maestro takes this step through Cadence’s AI Super Agents, which “interpret design intent, decompose tasks” and “orchestrate the Maestro portfolio with other leading frontier and open-weight models,” evaluating trade-offs to guide decisions with domain expertise. It also uses reinforcement learning (RL), where Cadence’s “deterministic solvers evaluate outputs against engineering objectives, constraints and ground truth,” and, importantly, the results feed back into the models for improvement.
The models include Nvidia’s Nemotron, and customers can run the customer-data training step themselves with Cadence’s fine-tuning harness, potentially “on their own GPUs, on their own network,” Cunningham said. He added that Nemotron, specifically, was “giving really solid results and actually improving very, very fast.”
One constant that keeps popping up with agentic EDA is the need to nail down what the numbers actually measure. In chip design, quality of results usually refers to the design’s power, performance, and area, though Cadence didn’t specify what its 30% figure measures. Both it and the 5x token efficiency figure are measured against “a general-purpose foundation model,” which Cadence told us is the open-source model Maestro started from before its training. The company wouldn’t name the foundational AI model that Maestro is derived from beyond mentioning that it is “one of the leading open source models that’s very competitive with commercial frontier models.”
Cadence credits the token savings to balancing its EDA and system design automation (SDA) “tool calls with LLM invocations.” As an analogy, Cunningham said, “You don’t need to drive to Safeway in the jet engine,” and you want to use “the right model at the right time for the right task.”
Runtime does matter, too: as Rob Knoth, senior group director of strategy and new ventures at Cadence, told Tom’s Hardware Premium in September that a tool call could take anywhere from hours to days to return a value, while the AI industry’s training loops are “used to minutes.” Efficiency could also lower costs, since Maestro use counts toward Cadence’s agent pricing, a subscription with a consumption allowance and charges for use beyond it, Cunningham sa