Companies and governments are looking for tools to run AI locally, creating AA bid slash cloud infrastructure costs and sovereign capabilities. QuadricA chip-IP startup founded by veterans of early bitcoin mining firm 21E6 is trying to power that transition by scaling beyond automotive devices to laptops and industrial devices, with its on-device inference technology.

That expansion is already paying off.

Quadric posts $15 million to $20 million in licensing revenue in 2025, up from about $4 million in 2024, CEO Birvan Khetarpal (pictured above, center) told TechCrunch in an interview. The company, which is based in San Francisco and has an office in Pune, India, is targeting up to $35 million this year as it builds a royalty-driven on-device AI business. That growth has boosted the company, which now has a post-money valuation of between $270 million and $300 million, up from about $100 million in its 2022 Series B, Khetarpal said.

It also helped in attracting investors to the company. Quadric announcement A $30 million Series C round led by Accelerate Fund led by BEENEXT Capital Management last week, bringing its total funding to $72 million. The growth comes as investors and chipmakers look for ways to push more AI workloads from centralized cloud infrastructure to devices and local servers, Kheterpal told TechCrunch.

Everything from automotive

Quadric Started in automotiveWhere on-device AI can power real-time functions like driver assistance. Khetarpal said that the proliferation of transformer-based models in 2023 has pushed projections “to everything,” creating a sharp business shift in the past 18 months as more companies try to run AI locally rather than relying on the cloud.

“Nvidia is a powerful platform for data-center AI,” said Kheterpal. “We wanted to create a similar CUDA-like or programmable infrastructure for on-device AI.”

Unlike Nvidia, Quadric doesn’t make its own chips. Instead, it licenses programmable AI processor IP, which Kheterpal describes as a “blueprint” that customers can embed in their own silicon, along with a software stack and toolchain to run the models, including vision and voice, on-device.

The startup’s customers span printers, cars and AI laptops, including Kyocera and Japanese auto supplier Denso, which makes chips for Toyota cars. The first products based on Quadric’s technology are expected to ship this year, starting with laptops, Khetarpal told TechCrunch.

Even so, Quadric is now exploring “sovereign AI” strategies outside of traditional commercial deployments and to reduce reliance on US-based infrastructure, Khetarpal said. The startup is looking for customers in India and Malaysia, he added, and counts Moglix CEO Rahul Garg as a strategic investor to help shape its India “sovereign” approach. Quadric employs about 70 people worldwide, including about 40 in the US and about 10 in India.

The push is being driven by the rising cost of centralized AI infrastructure and the difficulty many countries face in building hyperscale data centers, Khetarpal said, fueling more interest in “distributed AI” setups where guesswork runs on laptops or small on-premise servers inside offices rather than relying on cloud-based services for each.

World Economic Forum indicated In a recent article on this shift, AI assumptions move closer to users and away from fully centralized architectures. Likewise, EY said A November report said the sovereign AI approach has gained traction as policymakers and industry groups push for in-house AI capabilities that span calculations, models and data rather than relying entirely on foreign infrastructure.

For chipmakers, the challenge is that AI models are evolving faster than hardware design cycles, Kheterpal said. He argued that customers need programmable processor IP that can keep pace with software updates without requiring costly redesigns every time architectures migrate from earlier vision-centric models to today’s transformer-based systems.

Quadric is pitching itself as an alternative to chip vendors like Qualcomm, which typically uses its AI technology inside its own processors, as well as IP providers like Synopsys and Cadence, which sell neural processing engine blocks. Kheterpal says Qualcomm’s approach can lock customers into their own silicon, while traditional IP suppliers offer engine blocks that many customers find difficult to program.

Quadric’s programmable approach allows customers to support new AI models through software updates rather than redesigning the hardware, giving it an advantage in an industry where chip development can take years. The model architecture migrates over a few months nowadays

Still, Quadric is still early in its build, with a handful of signed customers so far, and much of its long-term growth depends on turning today’s licensing deals into high-volume shipments and recurring royalties.