Multiverse Computing, the Spanish quantum-AI scaleup, has announced funding commitments of up to $570 million in its latest round, putting the company at a $1.7 billion valuation. That figure represents a five-fold increase over the valuation it carried during its Series B, when it raised $215 million. Once the current round closes, total lifetime funding will reach approximately $800 million.

Who's Backing It

The round is co-led by Forgepoint Capital International, BNPP SIVF, and Bullhound Capital, with additional commitments from:

  • Santander Alternative Investments
  • Tikehau Capital
  • Orange Ventures
  • Scania Invest

The round remains open, meaning the final figure could shift before close.

The Core Bet: LLM Compression Meets Edge AI

Multiverse's central thesis is that AI is rapidly migrating from centralized data centers to edge devices — phones, drones, satellites, smart cameras, and vehicles. Processing data at the edge reduces latency, cuts energy consumption, and lowers compute costs. The bottleneck, historically, has been that large language models are simply too big to run efficiently on constrained hardware.

That's where Multiverse's flagship technology, CompactifAI, comes in. Built on principles from quantum physics — specifically tensor network methods borrowed from quantum many-body systems — CompactifAI compresses LLMs by 80–95% with what the company describes as "immaterial accuracy loss." The result is a model that's a fraction of the original size but retains most of its capability, making it viable for deployment on hardware that would otherwise be inadequate.

Already in Production Across Key Sectors

This isn't a lab project. Multiverse says CompactifAI is already running across drones, cameras, satellites, vehicles, and telecom infrastructure. Its customer and partner base spans manufacturing, finance, energy, aerospace, cybersecurity, defense, and health and life sciences, with named partners including Allianz, Bank of Canada, Bosch, Iberdrola, Indra, PwC, and Telefónica.

That breadth of enterprise deployment is significant — it suggests the technology has passed the proof-of-concept stage and is beginning to scale across regulated, high-stakes environments.

What Investors Are Seeing

"Multiverse sits at the intersection of the infrastructure and the application layers and has evolved from being the leading downstream LLM compression technology to becoming a complete AI foundry and Operating System. It is the only company we've seen that has both the technical foundation and the commercial traction to be that critical platform." — Damien Henault, Managing Director & Partner, Forgepoint Capital International

Henault's framing — "AI foundry and Operating System" — signals that investors aren't just betting on compression as a feature. They're backing Multiverse as a platform play that could sit beneath a wide range of enterprise AI deployments.

Why This Matters for Founders and Operators

For enterprise AI builders, Multiverse's raise is a signal that model efficiency is becoming a genuine infrastructure layer, not just an optimization trick. As compute costs remain high and edge deployments grow more common, compression technology that preserves model performance could become as foundational as inference hardware itself.

The quantum-physics angle is also worth watching. Most LLM compression research draws from classical machine learning; Multiverse's use of tensor networks gives it a differentiated technical foundation that's harder to replicate with standard ML tooling.

For startups building on top of LLMs — particularly those targeting enterprise or industrial edge use cases — the broader market shift Multiverse is capitalizing on is real: the economics of AI are pushing workloads away from hyperscaler infrastructure and toward leaner, embedded deployments. Companies that solve for that constraint are likely to attract continued investor attention.