European AI infrastructure company Multiverse Computing has secured a $570 million (€500 million) Series C funding round, pushing its pre-money valuation to $1.7 billion (€1.5 billion) as demand grows for AI models that require significantly less computing power. The investment positions the company to expand its AI compression platform, CompactifAI, at a time when enterprises and governments are seeking more efficient and sovereign alternatives to cloud-centric artificial intelligence deployments.

Multiverse Computing has raised $570 million (€500 million) in a Series C funding round that values the company at $1.7 billion (€1.5 billion) pre-money, marking a fivefold increase from its previous funding round. The investment underscores growing investor confidence in technologies designed to reduce the infrastructure costs associated with deploying large language models (LLMs) while enabling AI applications to run across cloud, on-premises and edge environments.

The financing was co-led by Forgepoint Capital International, BNP Paribas Solar Impulse Venture Fund (BNPP SIVF) and Bullhound Capital, with participation from strategic investors including HP Inc., Orange Ventures, Santander Alternative Investments, Scania Invest, National Bank of Canada’s NAventures, Qatar Development Bank, Tikehau Capital and several European institutional investors. Advised by J.P. Morgan and Santander Corporate & Investment Banking, the round is expected to bring the company’s total funding to approximately $800 million, with additional strategic investment opportunities remaining open.

The announcement reflects one of the most significant funding rounds in Europe’s AI sector this year and highlights increasing demand for software that can improve AI efficiency rather than relying solely on expanding GPU capacity.

At the centre of Multiverse Computing’s strategy is CompactifAI, a model compression platform that applies tensor network mathematics, originally developed in quantum physics, to reduce the size of large language models by as much as 80% to 95% while maintaining comparable performance levels. By compressing models, the company aims to lower memory requirements, reduce inference costs and enable AI applications to run directly on devices ranging from smartphones to industrial equipment.

Unlike traditional AI deployments that depend heavily on hyperscale cloud infrastructure, CompactifAI is designed to support inference across multiple environments. Its routing technology determines whether workloads should execute locally or in the cloud, allowing enterprises to optimise performance, latency and operational costs according to application requirements.

The approach aligns with one of the fastest-growing trends in enterprise artificial intelligence: edge AI. As manufacturers, telecommunications providers and industrial organisations deploy AI closer to users and connected devices, reducing model size has become increasingly important for applications where bandwidth, latency or data sovereignty limit cloud connectivity.

The company is also expanding beyond model compression into broader AI infrastructure software. Its platform integrates compressed AI models, GPU orchestration, deployment management and sovereign AI controls into a unified environment designed for enterprises and government-backed AI infrastructure projects. Rather than replacing existing cloud infrastructure, the software is intended to optimise how organisations allocate workloads across available computing resources.

The funding arrives amid intensifying competition in AI infrastructure. Technology leaders including NVIDIA, Microsoft, Amazon, Google and Meta continue investing billions of dollars in GPU clusters and hyperscale data centres to support increasingly sophisticated foundation models. At the same time, a growing ecosystem of AI infrastructure companies is focusing on improving efficiency through model optimisation, inference acceleration and hardware-aware software.

Another major theme driving investor interest is sovereign AI. Governments and regulated industries are increasingly seeking AI platforms that allow sensitive workloads to remain within national borders while reducing dependence on international cloud providers. Europe, in particular, has prioritised digital sovereignty through investments in domestic AI capabilities, high-performance computing infrastructure and regulatory frameworks such as the EU AI Act.

According to IDC, worldwide spending on AI-centric systems is expected to exceed $300 billion in the coming years as organisations accelerate enterprise AI adoption. Meanwhile, McKinsey & Company estimates that generative AI could contribute between $2.6 trillion and $4.4 trillion annually to the global economy, making infrastructure efficiency a critical competitive differentiator for organisations deploying AI at scale.

Since completing its Series B funding in mid-2025, Multiverse Computing says it has increased annualised revenue by more than tenfold while recording 96-times year-over-year sales growth during the first quarter of 2026. The company reports that its compressed AI models are already being deployed across applications including drones, cameras, satellites, connected vehicles and telecommunications infrastructure, with future deployments planned for AI-enabled personal computers.

Its customer base spans industries including manufacturing, financial services, energy, aerospace, cybersecurity, defence and healthcare, with organisations such as Allianz, Bosch, Iberdrola, Indra, PwC and Telefónica among those working with the company.

Looking ahead, the new capital will support expansion of Multiverse Computing’s efficient AI model portfolio, continued research into proprietary compression algorithms and broader investment in sovereign AI infrastructure. The company also plans to expand operations across East Asia, Southeast Asia, the Middle East, Canada and the United States as enterprises increasingly seek AI platforms capable of balancing performance, cost efficiency and regulatory compliance.

As generative AI adoption continues to accelerate, technologies that reduce computing requirements while maintaining model quality are likely to play an increasingly important role in determining how organisations deploy AI beyond hyperscale cloud environments.

Market Landscape

Enterprise AI is entering a phase where efficiency is becoming as important as model capability. Organisations are increasingly balancing performance with infrastructure costs, energy consumption and regulatory compliance. As edge AI, sovereign AI and AI PCs gain momentum, model compression and inference optimisation are emerging as strategic technologies alongside GPU innovation. Companies that improve AI deployment economics could become critical partners across manufacturing, finance, healthcare and government sectors.

Top Insights

• Multiverse Computing secured a $570 million Series C round, reaching a $1.7 billion valuation and reinforcing investor confidence in AI infrastructure focused on efficiency rather than raw computing scale.
• CompactifAI uses tensor-network mathematics to compress large language models by up to 95%, enabling faster inference, lower energy consumption and deployment across cloud, edge and on-device environments.
• The funding reflects growing demand for sovereign AI platforms that help enterprises and governments deploy AI while maintaining greater control over infrastructure, data residency and operational costs.
• Multiverse is expanding beyond model compression into a broader AI software platform combining GPU orchestration, workload routing and deployment management for enterprise AI environments.
• Rising enterprise AI adoption and increasing infrastructure costs are creating opportunities for technologies that optimise AI performance without requiring additional hyperscale computing resources.

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