DeepSeek Paralyzed for 12 Hours: Is the Computing Power of Domestic Large Models Failing to Keep Up with Ambitions?
On the evening of March 29, DeepSeek, a leading Chinese large language model, experienced a 12-hour service outage affecting both its web and app platforms. The disruption, marked by repeated server failures and instability, raised significant concerns about the platform's reliability.
Initial explanations pointed to overwhelming user traffic, but data did not support a sudden surge in users. Instead, the incident highlighted deeper structural challenges, particularly in computational supply struggling to keep pace with growing AI demands. As models become more complex—supporting longer context, advanced reasoning, and multimodal tasks—the computational load increases substantially.
The outage also underscored emerging usage patterns like “lobster farming,” where automated, high-frequency API calls amplify server load. Meanwhile, anticipation for DeepSeek’s upcoming V4 model—featuring major upgrades like million-token context and stronger multimodal capabilities—suggests even greater future computational pressure.
This incident signals a shift in the AI industry: competition is no longer just about model capability but also infrastructure stability, cost efficiency, and engineering scalability. The outage serves as an early warning of the systemic challenges facing AI platforms as adoption expands.
marsbit04/03 12:23
