China unveils ‘world’s first’ brain-like AI, 100x faster on local tech

Researchers at the Chinese Academy of Sciences’ Institute of Automation in Beijing have developed SpikingBrain 1.0, a “brain-like” large language model (LLM) that operates up to 100 times faster than conventional AI models while using significantly less training data—less than 2% of what typical models require. Unlike mainstream Transformer-based LLMs, which face efficiency bottlenecks due to quadratic scaling of computation with sequence length, SpikingBrain 1.0 employs “spiking computation,” mimicking biological neurons by firing signals only when triggered. This event-driven approach reduces energy consumption and accelerates processing, enabling the model to handle extremely long sequences of data efficiently. The team tested two versions of SpikingBrain 1.0, with 7 billion and 76 billion parameters respectively, trained on roughly 150 billion tokens—a relatively small dataset for models of this size. In benchmarks, the smaller model processed a 4-million-token prompt over 100 times faster than standard systems