Velaura AI Inc., a developer of low-power artificial intelligence chips, said today it has raised $110 million in Series A funding led by Seligman Ventures, with participation from Samsung Catalyst Fund, Mayfield and more than a half-dozen other investors. The round values the company at more than $1 billion.
The deal comes six months after Velaura pivoted from crypto mining hardware to AI chip design. The company, formerly known as Auradine, now sells Titan Core, a suite of processor building blocks and services that help customers build energy-efficient AI chips.
From bitcoin mining to AI infrastructure
Velaura's background in crypto mining shaped its approach to power efficiency. Its most advanced bitcoin chip, the liquid-cooled Teraflux AH3880, performs up to 600 trillion computations per second and includes a feature called EnergyTune that lowers energy use when grid capacity is tight.
The company applied that same efficiency focus to Titan Core. Large language models rely heavily on matrix multiplications, the mathematical operations that generate responses to prompts. Velaura says those calculations and related operations can account for up to 70% of an AI chip's power usage. The company claims Titan Core cuts the energy required for those operations by a factor of two to four, which it says can save up to $1,300 per chip over three years.
How Titan Core works
Chip designers build processors from cells, which are sets of transistors that perform specific tasks such as storing bits. Titan Core includes a cell library - a collection of pre-packaged cell designs - so customers don't have to develop those components from scratch. Velaura says the modules are optimized to run at low voltage.
Customers with more advanced requirements can commission custom low-voltage circuits. According to Velaura, chip teams only need to provide an RTL file, a high-level processor blueprint that doesn't specify how cells are implemented. The company also provides a "proprietary toolflow," a set of technical assets that help engineers improve chip reliability and manufacturing yield.
Titan Core supports multiple manufacturing processes, including the industry's latest three- and two-nanometer nodes. Velaura said it is working with several hyperscalers on chip projects using those technologies. Beyond data centers, the company sees customers using its low-voltage technology for edge devices.
"Every advance in AI, from reasoning models to embodied intelligence, creates demand for more compute, and ultimately more power," said Velaura Chief Executive Officer Rajiv Khemani. "The next era of AI will be defined not only by better models, but also by fundamentally better compute economics."
Velaura will use the new funding to accelerate engineering initiatives and expand its customer-facing teams. For developers and IT professionals, the company's approach points to a broader shift: as AI workloads grow, the economics of inference will increasingly depend on hardware efficiency, not just model architecture. Engineers working on AI infrastructure may want to track how low-power chip design affects deployment costs and data center power requirements. For those looking to deepen their understanding of AI systems, the AI Learning Path for Software Developers covers the fundamentals of building and deploying AI applications.
Why this matters for IT and development professionals
Power consumption is becoming a bottleneck for AI deployments. If Velaura's efficiency claims hold up, its technology could reduce the cost of running large models at scale - a factor that directly affects infrastructure budgets and capacity planning. Teams evaluating AI hardware should pay attention to how chip-level efficiency claims translate into real-world savings, especially as data center power constraints become more common. Those working in IT and development roles can find more resources on AI for IT & Development to stay current on infrastructure trends.
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