EnCharge AI Raises $100 Million To Challenge The AI Chip Status Quo With In-Memory Computing.

For decades, AI computing has revolved around the same fundamental bottleneck: transferring massive amounts of data between memory and processors, using both power and time in the process. While businesses like Nvidia and AMD have prospered in this climate, a new breed of chip startups is taking a different approach, one that completely eliminates inefficiency.er.

EnCharge AI, a Santa Clara-based startup that has just raised $100 million in Series B funding, bringing its total to $144 million. The round was led by Tiger Global, with participation from a long list of investors, including Maverick Silicon, Capital TEN, SIP Global Partners, Zero Infinity Partners, CTBC VC, Vanderbilt University, Morgan Creek Digital, Samsung Ventures, and HH-CTBC. Previous backers like RTX Ventures, Anzu Partners, Scout Ventures, AlleyCorp, ACVC, and S5V also doubled down on their investment.

Founded in 2022, EnCharge AI is built on nearly a decade of research from CEO and co-founder Naveen Verma, whose work at Princeton University explored the potential of non-volatile memory devices—a type of memory that retains information even when power is removed. This research has now evolved into an AI accelerator chip that integrates memory and computation, significantly reducing energy consumption while improving efficiency.

A Different Kind of AI Chip

The AI hardware market has largely been defined by GPUs, which have become the backbone of model training and inference. But GPUs are designed for high-performance parallel computing, not necessarily for power-efficient AI inference, especially in edge computing environments where real-time decision-making is needed.