German computing startup Sparxity has closed its Series A funding round, bringing its total funding to €80 million ($90 million). The company was previously known as SpiNNcloud and has now changed its name to Sparxity. It develops computing technology designed to process data more efficiently.
The funding round was led by ICM HPQC Fund, with participation from Lockheed Martin Ventures, GlobalFoundries, GS Futures, Cloudberry Ventures, TGFS and the EIC Fund. Angel investors Richard Socher of Recursive Superintelligence and Nancy Xu, founder of Moonhub, also joined the round. Existing investors Atlantic and Silicon Catalyst continued their support.
“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,” says 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 datacenters with dedicated silicon for ultra-sparse workloads to unlock greater AI performance and lower costs.”
“Our work with PEER demonstrated the potential of dynamic sparsity, activating only a fraction of the compute across millions of fine-grained experts,” adds Xu Owen He, former Google DeepMind researcher and Sparxity strategic advisor. “Yet today’s hardware limits how far we can take these approaches. Co-designing AI models, distillation pipelines, and hardware around sparsity unlock a new level of computing efficiency.”
Founded in 2021, Sparxity develops computing technology for artificial intelligence (AI) that helps systems work more efficiently. Its technology is already being used in large-scale computing systems and supercomputers across Europe and the US. The company aims to provide faster and more efficient computing solutions for future AI development.
Sparse computing helps reduce unnecessary calculations and data movement, saving both time and energy. Sparxity uses this approach in high-performance computing (HPC) and AI systems. According to the company, its technology can make certain tasks up to 10 times faster, reduce energy use by 18 times in event-based machine learning and cut the running time of selected optimisation tasks by up to 20 times.
“Sparxity has a unique complementary positioning at the infrastructure stack. Its architecture is grounded in first principles, addressing the inevitable shift toward sparse activation and the structural limitations of conventional hardware,” says Matthew Gould of ICM HPQC Fund.
The company will use the funding to develop specialised chips that can process AI tasks more efficiently. These chips aim to make AI models run up to 44 times faster while using less energy by focusing only on the data and calculations that matter.
