Sparxity, the German sparse-native compute company formerly known as SpiNNcloud, has raised $90 million following the close of a Series A funding round.

The round was led by ICM HPQC Fund and saw participation from Lockheed Martin Ventures, GlobalFoundries, GS Futures, Cloudberry Ventures, TGFS, and the EIC (European Innovation Council) Fund.

In a statement, Sparxity said the funding would support the development of custom chips for “fine-grained dynamic sparsity,” targeting up to 44× faster AI inference in ultra-sparse models and allowing customers to address rising AI infrastructure costs and energy consumption.

Sparxity was founded in 2021 under its former brand identity and is focused on building full-stack systems that “compute only what matters.” The company said this approach brings greater energy efficiency as it minimizes the amount of computing and data needed to be moved around, reducing bottlenecks.

Between 2024 and 2025, the company claimed it had quadrupled its revenue, and thus far has overseen three tapeouts, more than 70,000 chips produced, and 12 racks deployed. At present, Sparxity has more than 30 customers globally, including some of the largest supercomputing centers.

In October 2026, the company announced the deployment of a sparse-native computing rack system at the Jülich Supercomputing Centre (JSC) in Germany. Last week, Sparxity also unveiled a partnership with Deutsche Telekom to bring its sparse-native technology into T-Systems’ data center infrastructure.

“The next era of AI will be defined not just by how much computing power we can deliver, but by how intelligently and efficiently we use it,” said Sparxity co-founder and CEO Hector Gonzalez. “This financing is a powerful validation of our vision for sparse-native computing and the complementary positioning of our systems. With the backing of leading, strategic investors, we’re accelerating the transition from dense, energy-intensive architectures to heterogeneous data centers with dedicated silicon for ultra-sparse workloads to unlock greater AI performance and lower costs.”