Auradine Raises $153M in Series C to Expand Beyond Bitcoin Chips
U.S.-based Bitcoin mining chip designer Auradine has raised $153 million in a Series C funding round, bringing its total capital raised to over $300 million since its founding in 2022.
The financing round was led by StepStone Group and included participation from both new and existing investors such as Maverick Silicon, Premji Invest, Samsung Catalyst Fund, Qualcomm Ventures, Mayfield, GSBackers, and Bitcoin mining firm MARA.
Auradine said the new capital will support its expansion into next-generation mining chips and AI infrastructure, including the launch of AuraLinks AI—a new business unit focused on open-standard networking solutions for data centers. The company said it aims to address the growing bandwidth and cooling requirements associated with AI workloads.
Auradine’s dual focus on Bitcoin and AI has attracted a broad base of semiconductor and infrastructure investors. Since launching its first-generation Teraflux Bitcoin miners in 2023, the company claims to have secured orders from more than 40 data center operators. Last month, it introduced the hydro-cooled AH3880 Teraflux miner, featuring an energy efficiency of 14.5 J/TH.
According to its 2024 annual report, MARA held $50.7 million in Auradine preferred stock as of December 31 and had advanced $84.5 million toward future purchases of Teraflux miners throughout the year.
The Series C raise comes as the mining industry faces the potential imposition of U.S. tariffs on Chinese-made crypto mining equipment. Such trade measures could disrupt long-standing procurement strategies that depend heavily on manufacturers like Bitmain and MicroBT, pushing U.S. buyers to either halt hashrate expansion plans or look for other hardware providers.
Auradine’s newly launched AuraLinks AI division includes engineers with backgrounds at top networking and cloud infrastructure companies, including Cisco, Juniper, Broadcom, Google, and Microsoft. The team will develop high-speed networking products designed to optimize AI workloads and improve energy efficiency at scale.
