DeepSeek touts new training method as China pushes AI efficiency

• DeepSeek unveils efficient AI framework to boost scalability and cut energy use
• New method addresses training instability and scalability in large AI models
• Anticipation grows for DeepSeek's next flagship R2 model, expected in February

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DeepSeek published a paper outlining a more efficient approach to developing AI, illustrating the Chinese artificial intelligence industry’s effort to compete with the likes of OpenAI despite a lack of free access to Nvidia Corp. chips.

The document, co-authored by founder Liang Wenfeng, introduces a framework it called Manifold-Constrained Hyper-Connections. It’s designed to improve scalability while reducing the computational and energy demands of training advanced AI systems, according to the authors.

Such publications from DeepSeek have foreshadowed the release of major models in the past. The Hangzhou-based startup stunned the industry with the R1 reasoning model a year ago, developed at a fraction of the cost of its Silicon Valley rivals. DeepSeek has since released several smaller platforms but anticipation is mounting for its next flagship system, widely dubbed the R2, expected around the Spring Festival in February.

Chinese startups continue to operate under significant constraints, with the US preventing access to the most advanced semiconductors essential to developing and running AI. Those restrictions have forced researchers to pursue unconventional methods and architectures.