Juniper Networks and Recogni Announce Venture Funding and Collaboration for High Performance, Energy-Efficient and Cost-Effective Multimodal Generative AI Inference Systems
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Scalable, innovative GenAI solutions will support the largest AI models at unprecedented per-user performance while driving down energy demands and improving accuracy
SUNNYVALE, Calif. and SAN JOSE, Calif., Nov. 12, 2024 /PRNewswire/ --Juniper Networks® (NYSE: JNPR), a leader in secure, AI-Native Networking, and Recogni, the Generative AI Inference company, have jointly announced Juniper's investment in Recogni and an ongoing collaboration. Juniper has participated in Recogni's $102 million Series C funding round, which has been co-led by Celesta Capital and GreatPoint Ventures.
The collaboration will focus on AI inference compute and will address the growing demand from hyperscalers, compute service providers and enterprises for a cost-effective, scalable, energy-efficient solution to run increasingly complex AI models across cloud environments and data centers. The innovation will leverage Recogni's patented AI inference accelerator, based on Pareto math, which is designed from the ground up to optimize energy efficiency and model accuracy.
"The market for multimodal GenAI inference is accelerating rapidly and Recogni is at the forefront of exploring power efficiency without compromising performance or accuracy," said Rami Rahim, CEO of Juniper Networks. "In this phase of inference innovation, cost-effectiveness and power efficiency—aligned with scalable fabric and network solutions—are extremely important. We were impressed with the team's expertise and ability to approach complex issues with new ideas, so providing venture funding and collaboration made a great deal of sense."
"Juniper's commitment to pushing the boundaries of networking aligns perfectly with Recogni's mission to make AI more economical and accessible," said Marc Bolitho, CEO of Recogni. "We are collaborating on a solution that will allow the world's largest models to run at unprecedented speed, accuracy, and efficiency—drastically lowering total cost of ownership for our customers."
With new capital and a vision shared with innovative organizations such as Juniper, Recogni will be well-positioned to drive AI innovation forward, empowering hyperscalers, cloud service providers and enterprises to scale AI economically and responsibly.
About Recogni
Recogni is building the highest-performance, most energy-efficient Generative AI inference systems to accelerate the world's AI ambitions. Headquartered in San Jose, California, with a global presence in North America and Europe, Recogni is leading the AI revolution with a focus on delivering fast, accurate, and cost-effective systems. To learn more, visit www.recogni.com.
About Juniper Networks
Juniper Networks believes that connectivity is not the same as experiencing a great connection. Juniper's AI-Native Networking Platform is built from the ground up to leverage AI to deliver exceptional, highly secure and sustainable user experiences from the edge to the data center and cloud. Additional information can be found at Juniper Networks (www.juniper.net), or connect with Juniper on X (Twitter), LinkedIn, and Facebook.
Juniper Networks, the Juniper Networks logo, Juniper, Junos, and other trademarks listed here are registered trademarks of Juniper Networks, Inc. and/or its affiliates in the United States and other countries. Other names may be trademarks of their respective owners.
Statement of Product Direction. Juniper Networks may disclose information related to development and plans for future products, features or enhancements, known as a Statement of Product Direction or Plan of Record ("POR"). These details provided are based on Juniper's current development efforts and plans. These development efforts and plans are subject to change at Juniper's sole discretion, without notice. Except as may be set forth in a definitive agreement, Juniper Networks provides no assurances and assumes no responsibility to introduce products, features or enhancements described in this website, presentation, meeting, or publication, nor is Juniper liable for any loss arising out of reliance on the POR. Purchasing decisions by third-parties should not be based on this POR, and no purchases are contingent upon Juniper Networks delivering any feature or functionality described in this website, presentation, meeting, or publication.
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SOURCE Recogni
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