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The Secret Behind 'Dumbing Down': Zhipu AI Reveals the Inevitable Pain of Scaling

Zhipu AI published a technical blog detailing inference anomalies encountered during large-scale GLM-5 Coding Agent serving, including gibberish, repetition, and rare character generation. The team traced the issues to KV Cache race conditions and HiCache loading timing defects, and proposed solutions along with Prefill optimization strategies.

Source量子位Author: 鹭羽
The Secret Behind 'Dumbing Down': Zhipu AI Reveals the Inevitable Pain of Scaling
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Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • Zhipu AI experienced output anomalies (gibberish, repetition, rare characters) in GLM-5 Coding Agent tasks due to high-load inference state management.
  • The team used speculative decoding metrics (spec_accept_length) to detect anomalies and established an online monitoring strategy.
  • Root causes were KV Cache race conditions in PD-separated architecture and HiCache loading timing defects; fixed via synchronization and process refactoring.
  • A novel LayerSplit scheme for hierarchical KV Cache storage improved throughput by 10%-132% under long context.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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