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📐SiliconANGLE AI
July 28, 2026
Funding & Investment

AI model compression startup Multiverse raises $570M at $1.7B valuation

Overview

Multiverse Computing SL, a startup working on technology that compresses artificial intelligence models so that they run more efficiently on less hardware, announced Monday it raised $570 million in Series C funding. The round was co-led by Forgepoint Capital International, BNPP SIVF, and Bullhound Capital. Santander Alternative Investments, Tikehau Capital, HP Inc.

Key Takeaways

  • SiliconANGLE UPDATED 11:00 EDT / JULY 28 2026 AI AI model compression startup Multiverse raises $570M at $1.

    7B valuation by Kyt Dotson Multiverse Computing SL , a startup working on technology that compresses artificial intelligence models so that they run more efficiently on less hardware, announced Monday it raised $570 million in Series C funding.

  • Multiverse's flagship product is CompactifAI , a compression technology that uses tensor networks, a mathematical framework from quantum physics, to efficiently shrink the size of AI models.

    The company claims that this breakthrough can reduce the hardware footprint of large language models up to 80% to 95% with minimal accuracy loss.

  • " In many cases, edge devices, such as smartphones and computers attached to sensors, must offload high-powered AI computing into the cloud when the local AI is insufficient.

    Having a model run locally reduces latency, the time it takes a question to be sent out to the cloud and an answer returned, and it also keeps sensitive information on the device so that it is never seen by the outside world.

  • Although it still requires significant amounts of RAM, at around 1 terabyte, it can run the LLM efficiently entirely on the central processing unit, without the need for an ultra-powerful graphics processing unit.

    Multiverse's platform covers the full spectrum of efficient AI deployment, including compressed models that can be run on devices and models optimized for the cloud and on-premises for higher efficiency.

  • The company's customers and partners include Allianz, Bank of Canada, Bosch, Iberdrola, Indra, PwC, and Telefónica, covering industries from manufacturing to finance, energy to aerospace, cybersecurity, defense and life sciences.

Stats & Key Facts

  • #Multiverse Computing SL, a startup working on technology that compresses artificial intelligence models so that they run more efficiently on less hardware, announced Monday it raised $570 million in Series C funding.
  • #] The post AI model compression startup Multiverse raises $570M at $1.
  • #SiliconANGLE UPDATED 11:00 EDT / JULY 28 2026 AI AI model compression startup Multiverse raises $570M at $1.
  • #7B valuation by Kyt Dotson Multiverse Computing SL , a startup working on technology that compresses artificial intelligence models so that they run more efficiently on less hardware, announced Monday it raised $570 million in Series C funding.
AI model compression startup Multiverse raises $570M at $1.7B valuation

SiliconANGLE UPDATED 11:00 EDT / JULY 28 2026 AI AI model compression startup Multiverse raises $570M at $1. 7B valuation by Kyt Dotson Multiverse Computing SL , a startup working on technology that compresses artificial intelligence models so that they run more efficiently on less hardware, announced Monday it raised $570 million in Series C funding. The round was co-led by Forgepoint Capital International, BNPP SIVF, and Bullhound Capital.

Santander Alternative Investments, Tikehau Capital, HP Inc. , Orange Ventures, Scania Invest, NAventures and half a dozen other investors also committed capital to the funding round. The raise values the company at a $1.

7 billion pre-money valuation, an almost five-fold step up from its $215 million Series B raise in June 2025. Multiverse's flagship product is CompactifAI , a compression technology that uses tensor networks, a mathematical framework from quantum physics, to efficiently shrink the size of AI models. The company claims that this breakthrough can reduce the hardware footprint of large language models up to 80% to 95% with minimal accuracy loss.

For more details please read the original article at SiliconANGLE AI.

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