Multiverse Computing Launches CompactifAI App, Revolutionizing Edge AI with Quantum-Inspired Model Compression

March 3, 2026
Multiverse Computing Launches CompactifAI App, Revolutionizing Edge AI with Quantum-Inspired Model Compression
  • Multiverse Computing debuts the CompactifAI App, a mobile solution that runs advanced AI models locally on devices offline or switches to cloud-based models via API, with a focus on edge AI.

  • The app enables fully offline edge operation, smart routing between on-device and API models to balance speed and capability, and privacy-by-design with local data processing.

  • This approach supports privacy-centered security and reduces reliance on cloud infrastructure for sophisticated reasoning.

  • Details on pricing, monetization, and initial customer adoption remain limited, making near-term financial impact uncertain and dependent on execution.

  • Multiverse Computing is a Donostia, Spain–based company with offices in the US, Canada, and Europe, serving more than 100 global customers including Iberdrola, Bosch, and the Bank of Canada.

  • Headquartered in Donostia, the company maintains a multinational presence and a sizable enterprise customer base across industries.

  • Multiverse showcased CompactifAI at MWC26 in the Spanish Pavilion, Hall 4, signaling efforts to broaden its commercial reach in edge AI and LLM tooling.

  • The launch follows the earlier release of HyperNova, an open-source compressed model, extending efficiency gains from research to real-world deployments.

  • HyperNova represents a stepping stone in Multiverse’s trajectory toward practical, efficient AI for deployment.

  • The LinkedIn post highlights privacy and security benefits, targeting users in regulated or data-sensitive sectors and scenarios with low connectivity.

  • CompactifAI uses quantum-inspired mathematics to compress AI models by up to 95% with only a 2-3% precision loss, outperforming typical compression trades.

  • The technology leverages Multiverse's CompactifAI approach to deliver high compression with minimal accuracy sacrifice, distinguishing it from standard methods.

Summary based on 6 sources


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