We talk with FuriosaAI’s June Paik about AI hardware, startup survival, and the future of inference chips

AI acceleration hardware is becoming increasingly expensive, with next-generation chips demanding high power budgets and major infrastructure investment. For many startups and smaller enterprises, the cost and complexity of deploying AI at scale creates barriers long before software development even begins.

New silicon players such as FuriosaAI are trying to rethink that equation, focusing on efficiency, performance, and alternative approaches to the GPU-dominated market.

Founded in South Korea, FuriosaAI develops AI inference chips designed to deliver high performance while reducing power consumption and data center strain. Its latest processor, RNGD, is built around the company’s Tensor Contraction Processor architecture and aims to run demanding AI models without relying on traditional GPU frameworks.