A Chinese team has unveiled what it calls the world’s first “brain-like” large language model – an artificial intelligence system designed to use less energy, perform better and operate without Nvidia chips. Developed by researchers at the Chinese Academy of Sciences’ Institute of Automation in Beijing, SpikingBrain 1.0 mimics how the human brain fires only the neurons it needs. Instead of activating an entire network like ChatGPT and other mainstream AI tools, it selectively responds to input, saving power and speeding up response time. Thanks to this design, the model can learn from just a sliver of training data – less than 2% of the amount conventional systems need – while staying fast and efficient even when processing long text. In some cases, it ran up to 100 times faster than traditional models, according to a non-peer reviewed technical paper posted on arXiv, an open-access research repository. The system runs entirely on China’s homegrown AI ecosystem, powered by the MetaX chip platform rather than Nvidia’s dominant GPU hardware. That makes the model strategically important as the U.S. tightens export controls on advanced AI chips.
Li Guoqi, Lead Researcher at the Institute, said the model opened a new path for AI development while delivering a framework optimized for Chinese chips. He said it could be useful to process long sequences of data such as legal documents, medical records or scientific simulations. Li’s team has open-sourced a smaller version of the model and made a larger one available online for public testing. “Hello! I’m SpikingBrain 1.0, or ‘Shunxi’, a brain-inspired AI model,” the system says on its demo site. “I combine the way the human brain processes information with a spiking computation method, aiming to deliver powerful, reliable, and energy-efficient AI services entirely built on Chinese technology.”
Today’s most popular AI models, including ChatGPT, require enormous computing power. To train them, companies rely on massive data centers packed with high-end chips that burn through electricity and cooling water. Even after training, these systems remain resource-hungry. Handling long inputs or generating complex responses can slow them down and strain memory, as they process every word in parallel rather than focusing only on what matters – driving up the cost and environmental impact of running them. In contrast, the team behind SpikingBrain 1.0 took inspiration from how real neurons work. Rather than processing everything at once, the system reacts selectively, using less power to perform complex tasks – much like the human brain. Its core technology, known as “spiking computation”, mimics the brain’s habit of firing quick bursts of signals only when triggered. This event-driven approach keeps the system quiet most of the time, helping it stay lean and energy-efficient, the South China Morning Post reports.
Meanwhile, two new Chinese artificial intelligence models have broken into a top 10 leaderboard for both open- and closed-source models, highlighting China’s progress in closing the gap with leading U.S. developers. Alibaba Group Holding’s 1 trillion-parameter Qwen3-max-preview model debuted in sixth place in the latest “text arena” ranking by LMArena – an AI model evaluation platform started by University of California, Berkeley researchers – making it the top Chinese model. Alibaba-backed start-up Moonshot AI’s updated Kimi-K2 model tied for eighth place with seven other models, including DeepSeek R1 and xAI’s Grok 4, reinforcing its status as one of the premier open-source models globally. A year ago, no Chinese models were represented in LMArena’s top 10 leaderboards, which is based on user feedback.
