DeepSeek Founder Unveils New Training Method Aimed at Overcoming GPU Memory Limits

A new research paper co-authored by Liang Wenfeng, founder of Chinese artificial intelligence start-up DeepSeek, is drawing attention within the global AI community for proposing an alternative approach to training large models under hardware constraints. The paper, produced in collaboration with researchers from Peking University, outlines a technique designed to bypass graphics processing unit memory limits while enabling rapid expansion in model parameters.

The proposal comes at a time when access to advanced computing power has become a defining factor in AI development. Leading US technology companies rely on vast clusters of high-end GPUs to train ever-larger models, an advantage that many Chinese firms lack due to supply constraints, export controls, and high costs. Against this backdrop, the DeepSeek research reflects a growing push within China’s AI sector to achieve progress through efficiency rather than brute-force scaling.

According to the paper, the new training approach allows models to handle significantly larger parameter sizes without requiring proportional increases in GPU memory. By restructuring how parameters are stored and updated during training, the method reduces memory pressure while preserving performance. The authors describe this as enabling aggressive parameter expansion, potentially allowing smaller teams to experiment with large-scale models that would otherwise be impractical.

Liang Wenfeng’s involvement highlights DeepSeek’s continued emphasis on cost efficiency. Founded in Hangzhou, the start-up has positioned itself as a challenger to US AI leaders by focusing on optimization, software-level innovation, and alternative training strategies rather than competing directly on hardware scale. Industry observers say this philosophy has helped DeepSeek gain attention despite operating with far fewer computational resources than its global rivals.

The collaboration with Peking University also underscores the increasingly close ties between Chinese academia and private AI firms. Universities have become important partners in foundational research, particularly as companies look for new ways to push technical boundaries under external constraints. Researchers involved in the paper argue that such cooperation is essential for sustaining innovation in a resource-limited environment.

The timing of the publication has fueled speculation within China’s tech sector. With the Lunar New Year approaching, analysts and developers are closely watching for signs of a major new model release from DeepSeek. While the company has not confirmed any launch plans, the paper suggests that internal research is actively exploring methods to scale models more efficiently, potentially laying the groundwork for future products.

The broader implications extend beyond a single company. If the proposed training technique proves effective in practice, it could influence how AI models are developed in regions facing similar hardware bottlenecks. Efficient training methods may also become more relevant globally as energy costs rise and environmental concerns prompt scrutiny of the resources consumed by large-scale AI systems.

Experts caution that translating theoretical advances into production-ready systems can be challenging. Memory optimization techniques often introduce trade-offs in training speed or complexity, and real-world performance will depend on implementation details. Still, the paper has been welcomed as a sign that innovation in AI training is not limited to those with the largest computing budgets.

As competition in artificial intelligence intensifies, DeepSeek’s latest research highlights an alternative path forward. Rather than relying solely on access to cutting-edge hardware, the company is betting that smarter training methods can help narrow the gap with global leaders and keep China’s AI ecosystem competitive in an increasingly constrained technological landscape.