BlackRock Leads the Investment Round to Further Accelerate Artificial Intelligence

PALO ALTO, Calif. - SambaNova Systems, the company building the industry’s most advanced systems platform to run artificial intelligence (AI) and data-intensive applications from the datacenter to the edge, announced today a $250 million Series C funding round to further accelerate the software capabilities of SambaNova’s next-generation computing platform. SambaNova Systems will continue to build out all areas of the organization, propelling the company into new market opportunities. The round is led by funds and accounts managed by BlackRock with participation from existing investors including Atlantic Bridge, GV, Intel Capital, Walden International, WRVI Capital and Redline Capital.

“Raising $250M in this funding round with support from new and existing investors puts us in a unique category of capitalization,” said Rodrigo Liang, co-founder and CEO, SambaNova Systems. “This enables us to further extend our market leadership in enterprise computing.”

SambaNova Systems offers an integrated hardware and software solution with an architecture optimized for dataflow from algorithms to silicon, enabling a broad range of compute-intensive applications to run from the datacenter to the edge. SambaNova Systems’ reconfigurable dataflow architecture enables applications to drive optimized hardware configurations. Software will no longer be confined by the constraints of fixed hardware.

In April of 2019, SambaNova Systems announced a Series B funding round of $150 million, led by Intel Capital with participation from GV. SambaNova Systems was founded in 2017 by Rodrigo Liang and Stanford Professors Kunle Olukotun and Chris Ré. Olukotun, known as the “father of the multi-core processor,” is the leader of the Stanford Hydra Chip Multiprocessor (CMP) research project. Ré is an associate professor in the Department of Computer Science at Stanford University in the InfoLab and a MacArthur Genius Award recipient. Ré is also affiliated with the Statistical Machine Learning Group, Pervasive Parallelism Lab, and Stanford AI Lab