Chinese AI Chip Firm Moxin AI Completes ~1 Billion RMB C+ Round, Q3 Revenue Grows Multiples
Moxin AI has completed a C+ round of approximately 1 billion RMB, just five months after its previous raise. Its sparse inference chips have won MLPerf benchmarks, and Q3 revenue grew multiples, signaling accelerated commercialization.
Recently, Chinese AI chip company Moxin AI announced the completion of a C+ round of financing totaling nearly 1 billion RMB. This marks the second consecutive large-scale raise within a short period, occurring just five months after its previous round of similar magnitude. The dense capital injection reflects strong market confidence in the company's technical barriers and commercial progress in the field of dedicated sparse inference chips.
As large model architectures evolve towards Mixture-of-Experts (MoE), computational loads exhibit distinct sparsity characteristics. For instance, DeepSeek-V4-Pro has approximately 1.6 trillion total parameters but activates only about 49 billion per token, roughly 3%. General-purpose GPUs struggle to deeply co-optimize for such sparse patterns, whereas dedicated sparse inference chips can reduce redundant computation and data movement overhead through algorithm-compiler-hardware co-design.
Since its founding in 2018, Moxin has focused on this direction, inventing a "weight sparsification + activation sparsification" dual-sparsity algorithm. In the authoritative MLPerf™ Inference benchmark, its S30 compute card has won three consecutive titles in certain test categories, maintaining industry-leading energy efficiency and inference throughput per unit of compute.
On the commercial front, Moxin's inference clusters built on its proprietary chips have been deployed across four major regions in China: Northwest, Southwest, East, and North. In Q3 2026, the company reported multiple-fold revenue growth. Specific implementations include participation in Shanghai's first offshore computing Token factory project, leveraging sparse architecture to lower per-token inference costs; and collaboration with Peking University's Shanghai Lingang International Science and Technology Innovation Center, achieving up to 2.88x higher inference throughput and 7.64x model compression in remote sensing image analysis tasks.