On-Device AI: How Quadric Is Powering the Next Computing Shift
Why are companies and governments rapidly moving AI off the cloud and onto devices? The answer boils down to three urgent drivers: cost, control, and sovereignty. As cloud-based AI infrastructure becomes more expensive and geopolitically sensitive, a new wave of on-device AI solutions is emerging—and San Francisco–based startup Quadric is at the forefront. With $15–20 million in licensing revenue in 2025 and major design wins in laptops, automotive systems, and industrial hardware, Quadric’s programmable AI processor IP is helping manufacturers embed intelligence directly into their silicon, without relying on distant data centers.
This isn’t just a technical pivot—it’s a strategic realignment of how AI gets deployed in the real world. And it’s happening faster than many predicted.
The Rise of On-Device AI: More Than Just a Cost Play
For years, AI ran almost exclusively in the cloud. Training massive models required vast GPU clusters; inference was routed through centralized servers. But that model is cracking under pressure. Cloud AI bills are soaring—especially for real-time applications like voice assistants or autonomous driving—while latency and privacy concerns persist.
Enter on-device AI: running inference directly on phones, laptops, cars, or factory robots. This approach slashes recurring infrastructure costs, improves response times, and keeps sensitive data local. It also aligns with national strategies around “sovereign AI,” where countries like India and Malaysia seek to reduce dependence on U.S.-based cloud platforms.
Quadric recognized this inflection point early. Founded by veterans of the pioneering bitcoin mining firm 21E6, the company built its entire architecture around flexibility and efficiency at the edge. “We weren’t trying to compete with Nvidia in the data center,” says CEO Veerbhan Kheterpal. “We wanted to build the CUDA of on-device AI—a programmable, software-defined platform that works across any chip.”
Quadric’s Secret Sauce: Programmable IP, Not Physical Chips
Unlike traditional semiconductor firms, Quadric doesn’t manufacture chips. Instead, it licenses AI processor intellectual property (IP)—a complete “blueprint” that partners can integrate into their own system-on-chip (SoC) designs. This includes not just the hardware architecture but also a full software stack, compiler, and runtime environment optimized for vision, audio, and multimodal models.
The result? A chip-agnostic solution that scales from low-power IoT sensors to high-performance AI laptops. Customers like Kyocera and Denso (a major supplier to Toyota) are already embedding Quadric’s IP into next-generation products set to ship in 2026.
What makes this approach powerful is its programmability. Rather than hardwiring support for specific neural networks, Quadric’s architecture adapts to evolving models—including transformer-based architectures that now dominate everything from speech recognition to object detection. That future-proofing is critical in an era where AI models update monthly, not yearly.
From Automotive Roots to Global Expansion
Quadric began in automotive, where split-second decisions can’t wait for a round-trip to the cloud. Advanced driver-assistance systems (ADAS), cabin monitoring, and predictive maintenance all benefit from local inference. But the explosion of efficient, compact transformer models in 2023 changed everything.
“All of a sudden, every device wanted AI,” Kheterpal explains. Printers needed smart document parsing. Industrial cameras required real-time defect detection. Laptops demanded offline voice transcription and background blur. The common thread? They all needed reliable, low-latency inference without constant internet connectivity.
That shift triggered Quadric’s rapid expansion beyond cars. In 2025, licensing revenue jumped nearly fivefold—from $4 million to $20 million—as design wins multiplied. The company now targets $35 million in revenue for 2026 and recently closed a $30 million Series C round led by ACCELERATE Fund, bringing total funding to $72 million.
Its post-money valuation has tripled since 2022, landing between $270 million and $300 million—a testament to investor confidence in the on-device AI thesis.
Sovereign AI: A Geopolitical Catalyst
Beyond economics, on-device AI is gaining traction as a tool of digital sovereignty. Nations wary of relying on U.S. or Chinese cloud infrastructure are investing in local AI ecosystems. India, for example, has launched initiatives to build domestic AI compute capacity, while Malaysia explores edge-based solutions for public services.
Quadric is positioning itself as a neutral enabler of this trend. With an office in Pune and strategic backing from Moglix CEO Rahul Garg—a vocal advocate for India’s tech self-reliance—the company is tailoring its go-to-market strategy for emerging markets. “Sovereign AI isn’t just about data residency,” Kheterpal notes. “It’s about having the tools to build, run, and control your own AI stack—on your own terms.”
This geopolitical tailwind is accelerating adoption in sectors like defense, healthcare, and government, where sending data to third-party clouds is no longer acceptable.
Real Products, Real Impact—Starting in 2026
While much of the on-device AI conversation remains theoretical, Quadric is crossing into commercial reality. The first consumer devices powered by its IP—AI-enhanced laptops from unnamed OEMs—are expected to hit shelves later this year. These machines will perform tasks like real-time translation, noise suppression, and gesture recognition entirely offline.
In automotive, Denso’s integration means Toyota vehicles could soon feature smarter, more responsive cabin systems that adapt to driver behavior without phoning home. Meanwhile, industrial clients are testing Quadric-powered vision systems that detect manufacturing flaws faster and more accurately than legacy setups.
Critically, all these applications run on standard CMOS processes, meaning manufacturers don’t need exotic or expensive fabrication techniques. That compatibility lowers barriers to entry and speeds time-to-market—a key advantage in today’s hypercompetitive hardware landscape.
Distributed AI Is Here to Stay
The World Economic Forum and EY have both highlighted the move toward distributed AI architectures, where intelligence is spread across billions of devices rather than concentrated in a handful of mega-data centers. Quadric’s growth mirrors this macro shift.
As AI models become more efficient and hardware more capable, the line between cloud and edge will blur—but the direction is clear: more processing will happen where the data originates. For businesses, that means lower costs and better user experiences. For nations, it means greater autonomy. And for users, it means faster, more private interactions with the digital world.
Quadric may be a startup, but its vision is expansive. By providing the foundational IP layer for on-device AI, it’s not just riding the wave—it’s helping build the surfboard. And in a world racing toward intelligent, independent devices, that’s a position worth watching closely.
