Spain-based Multiverse Computing, which uses quantum-inspired AI model compression tech to shrink LLMs by up to 95%, raised a €189M Series B and has 100 clients
Spanish startup Multiverse Computing on Thursday said it has raised an enormous Series B round of €189 million …
Context & Ripple Effects
Multiverse Computing had already raised a €25M Series A for its tensor-network approach to LLM compression; this much larger round marks a sharp escalation in backing for that same efficiency proposition. The company now says it has 100 clients, making the financing relevant beyond a purely research-stage claim.
The story sits in an arc that later included reports of Multiverse pursuing a far larger raise after reaching €100M ARR, suggesting investors increasingly value software that reduces the compute and energy burden of deploying AI models.
First-order effects
- Multiverse gains €189M to expand its model-compression product and support its existing customer base, while its claimed ability to shrink LLMs by up to 95% becomes the central commercial benchmark for the company.
- Customers evaluating smaller or cheaper-to-run model deployments gain a better-capitalized specialist supplier rather than relying solely on model providers’ native optimization tools.
Second-order effects
- LLM infrastructure and optimization vendors face stronger pressure to demonstrate measurable reductions in model size, compute use, or deployment cost; compression becomes a more visible buying criterion.
- The round strengthens the case for spending on the software layer that makes existing AI hardware go further, rather than treating additional compute capacity as the only route to AI scale.
Third-order effects
- If enterprise adoption continues, AI infrastructure spending may shift toward a two-part stack: capital-intensive compute supply alongside specialized efficiency software that lowers the compute required per workload.
- The financing trajectory—from the earlier €25M Series A to this round and later reported growth—points to a potential consolidation dynamic in AI optimization, where customer traction and proven savings may determine which specialists endure.
The trend: AI investors are increasingly financing efficiency technologies that aim to reduce the cost and resource intensity of deploying large models, not just the compute used to train them.