Lessons from Debugging GLM-5 at Scale (z.ai)

A recent analysis of the GLM-5 series has highlighted significant challenges encountered while scaling the inference infrastructure for Coding Agent tasks, particularly during high-concurrency, long-context workloads. Users reported abnormal outputs such as garbled text and repetition, prompting an extensive investigation that revealed several low-level race-condition bugs in the system. By simulating peak conditions and meticulously logging performance metrics, the team established that these anomalies were linked to mismanagement of KV Cache during intense load, rather than intrinsic model errors. This understanding led to targeted optimizations, including a novel anomaly detection strategy using speculative decoding metrics to monitor output quality in real-time. The implications of these findings are profound for the AI/ML community, as they underscore the importance of robust infrastructure in supporting large-scale deployments of AI models. The fixes implemented, such as synchronizing KV Cache operations and establishing timeout mechanisms for requests, significantly reduced the abnormal output rate—from approximately 0.1% to below 0.03%. This case exemplifies the growing demand for sophisticated infrastructure solutions in AI, particularly as models expand in complexity and application, and sets a precedent for addressing similar scaling issues in future AI systems.