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Customers turn to SambaNova to quickly deploy state-of-the-art AI capabilities to meet the demands of the AI-enabled world. SambaNova’s purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing. SambaNova Systems enable customers to unlock the valuable business insights trapped in their data. The Company’s flagship offering, Dataflow-as-a-Service, overcomes the limitations of legacy technology to power the large complex foundation models that enable customers to discover new services and revenue streams, and boost operational efficiency. Headquartered in Palo Alto, California, SambaNova Systems was founded in 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University.

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SambaNova Systems has been making significant strides in the AI industry, showcasing its innovative capabilities and positioning itself as a formidable player in the market. The company recently unveiled a high-speed demo powered by Meta's Llama 3.1 Instruct model, directly challenging OpenAI's offerings. This demonstration highlights SambaNova's commitment to providing developers with fast, open-source alternatives in the generative AI space.

Building on this momentum, SambaNova Systems launched its new inference cloud featuring Meta's Llama 3.1 405B model. This service offers both free and paid enterprise tiers, with plans to introduce a developer tier later this year. This strategic move further solidifies SambaNova's position as a key player in the AI infrastructure landscape, catering to a wide range of users from individual developers to large enterprises.

In a groundbreaking development, SambaNova Systems announced a major leap in AI performance with its Reconfigurable Dataflow Architecture. This innovative technology achieves an impressive 10X performance boost while consuming only one-tenth of the power compared to traditional systems. This breakthrough not only demonstrates SambaNova's technical prowess but also addresses critical issues of efficiency and sustainability in AI computing.

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