Current Role
Co-founder and CEO of Cerebras Systems, building the world's fastest AI infrastructure.
Co-founder & CEO / Cerebras Systems
Andrew Feldman is the Co-founder and CEO of Cerebras Systems, a company specializing in building the world's fastest AI infrastructure, including the revolutionary Wafer Scale Engine (WSE-3), the largest processor in the history of the computer industry. He is a serial entrepreneur, having previously co-founded and served as CEO of SeaMicro, which was acquired by AMD for $355 million, and held executive roles at Force10 Networks and Riverstone Networks. Feldman is dedicated to solving hard problems and transforming the data center and AI compute space.
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Founder and CEO
Corporate Vice President and General Manager
Chief Executive Officer
Entrepreneur In Residence
Entrepreneur In Residence
Vice President, Marketing and Product Management
Vice President Corporate Marketing and Corporate Development
Sr. Dir Marketing
Vice President Sales and Marketing
Master of Business Administration (MBA)
BA
Co-founder and CEO of Cerebras Systems, building the world's fastest AI infrastructure.
Pioneered the high-density, low-power microserver category as CEO of SeaMicro (acquired by AMD for $355M).
Led two business units at AMD (Data Center Server Solutions and Server CPUs) after the SeaMicro acquisition.
Held VP roles at Force10 Networks and Riverstone Networks, contributing to significant corporate growth and an IPO.
Andrew Feldman's expertise spans founding and leading multiple successful technology companies, specializing in high-performance computing, AI infrastructure, and data center transformation.
Discusses Cerebras's latest funding round and the company's mission to build the fastest AI machines for both inference and training, emphasizing the company's technological edge.
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Andrew Feldman discusses Cerebras's massive $1.1 billion fundraise at an $8.1 billion valuation and the company's ambitious mission to build the fastest LLM chips on Earth.
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Feldman explains how wafer-scale computing overcomes the limitations of traditional chips, arguing that the future of AI compute lies in using an entire silicon wafer as a single processor.
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