Seoul-based AI chip startup FuriosaAI raised a $620M Series D at a $2.9B valuation, up from $735M in 2025; South Korea's National Growth Fund contributed $280M
South Korean AI chip startup FuriosaAI quadrupled its valuation to $2.9 billion in a fresh funding round, bolstering its bid to challenge the likes of Nvidia Corp.
Context & Ripple Effects
FuriosaAI had already chosen independence over Meta’s $800M takeover approach and converted RNGD into its first major commercial foothold through an LG deal. That made its planned Series D less a seed-stage bet than a financing step toward production and customer deployment.
January reporting framed the round as a $300M-$500M effort to fund mass production of FuriosaAI’s second-generation RNGD chip; the completed financing exceeds that target. The National Growth Fund’s $280M contribution also makes public capital a material part of the company’s expansion plan.
First-order effects
- FuriosaAI gains $620M to support chip production and next-generation silicon development, with a $2.9B valuation giving it a substantially larger capital base for competing in AI inference hardware.
- The National Growth Fund becomes a major financial backer of FuriosaAI, tying a significant share of the round to South Korean growth capital rather than private investors alone.
Second-order effects
- Nvidia faces a better-funded independent challenger for inference-chip deployments, while enterprise buyers have a supplier with more capacity to finance product development and commercialization.
- For DeepX and other South Korean AI-chip designers, FuriosaAI’s financing creates a more demanding benchmark for valuations and the scale of capital investors may expect behind production plans.
Third-order effects
- If public-backed rounds of this scale recur, South Korea’s AI-chip sector may compete less as a collection of venture-funded startups and more as a strategically financed supply base for AI infrastructure.
- The funding reinforces a market in which inference-chip challengers need both customer validation and large balance sheets to turn specialized designs into production-scale alternatives.
The trend: AI inference hardware is becoming an infrastructure-finance market, with public and private capital concentrating behind vendors that can move from chip design to scaled deployment.