The $154M Series C funding round will be used to deliver advanced photonic compute technology to the world’s leading cloud providers, semiconductor companies, Global 1000 enterprises and governments. Lightmatter’s solution set will enable a new level of performance and energy savings to the world’s most advanced AI and HPC workloads.

Existing compute technology is not designed to power AI data processing and its associated energy consumption. Generative language models are trained on trillions of examples of text with systems that massively increase operating costs, energy usage and heat.

Cloud providers, semiconductor companies and enterprises need an HPC solution capable of supporting generative AI, advanced robotics and telecommunications. The company’s full stack of hardware and software solutions (Envise, Passage, and Idiom) deliver that performance and energy, utilizing the most advanced photonic compute and interconnect technologies.

“Lightmatter’s unique approach to harnessing the power of photonics will drive the enormous compute required for generative AI. The massive global growth in these technologies and use cases is at the forefront of this absolutely essential paradigm shift,” stated Jeffrey Smith, General Partner of SIP Global Partners. “We are especially excited to see LM’s early entry into the Japanese market. We expect them to play a leading role as Japanese AI use cases grow exponentially.”

“Rapid progress in artificial intelligence is forcing computing infrastructure to improve at an unprecedented rate. The energy costs of this growth are significant on a planetary scale,” said Lightmatter co-founder and CEO, Nick Harris. “Generative AI and supercomputing will be transformed by photonic technologies in the coming years. We look forward to enabling the Japanese AI ecosystem with Lightmatter’s next-generation computing technologies.”

There is a desperate need for computing solutions that are capable of solving compounding AI workloads. Silicon photonics has the potential to provide the cost and environmental efficiencies necessary to support AI and its applications.