Chip designer Velaura AI valued at more than $1 billion in funding round
FILE PHOTO: Computer motherboard and chip in this illustration taken August 25, 2025. REUTERS/Dado Ruvic/Illustration/File Photo
Aug 18 : Velaura AI said on Tuesday it raised $110 million in a Series A funding round that valued the chip-designing startup at more than $1 billion, with investors backing its technology that can lower power consumption and operating costs at AI data centers.
The funding round was led by Seligman Ventures, with participation from new investor Capricorn Investment Group. Existing investors Samsung Catalyst Fund, StepStone Group and Maverick Silicon also participated.
Here are some details:
• Velaura AI develops low-power chips and software technologies for data centers and so-called physical AI applications, including robotics and autonomous systems.
• The startup said the funds will be used to speed up development and deployment of its AI products and hire engineers and customer-facing staff.
• Earlier this year, Velaura announced Titan Core, its proprietary chip-design platform targeting greater efficiency and power savings in data center workloads.
• "The next era of AI will be defined not only by better models, but also by fundamentally better compute economics," Rajiv Khemani, co-founder and CEO of Velaura AI, said in a statement.
• Velaura is engaged with three of the four largest cloud computing providers as potential customers, Khemani told Reuters, but he declined to name them. The company said its technology has already been deployed in more than 30 million chips.
• The startup charges an upfront fee for its technology, plus a royalty tied to a share of the power savings customers achieve — a structure Khemani confirmed is similar to Arm's per-chip licensing model before it started selling its own chips.
• Umesh Padval, managing partner at Seligman Ventures, which led the investment, said Velaura was positioned to benefit from both the rising power demands of AI data centers and growing demand for energy-efficient computing in robotics.
