Z.ai@Zai_org · X模型能力的提升遵循扩展定律，但其在生产环境中的可靠性取决于如何应对“规模化阵痛”。博客通过GLM-5大规模服务的调试实例，分享了处理罕见乱码输出、重复及生僻字符生成等问题的经验。关键工作包括追踪并消除KV Cache的竞态条件、修复HiCache同步问题，以及引入LayerSplit技术以实现最高132%的吞吐量提升。这些实践旨在帮助社区避免类似陷阱，构建更健壮的推理基础设施。
Scaling laws push model capability forward. But whether that capability becomes reliable in production depends on how we handle Scaling Pain.
http://z.ai/blog/scaling-pain In our latest blog， we share how we debugged GLM-5 serving at scale： reproducing rare garbled outputs， repetition， and rare-character generation； tracing and eliminating KV Cache race conditions； fixing HiCache synchronization issues； and introducing LayerSplit for up to 132% throughput improvement.
We hope these lessons help the community avoid similar pitfalls and build more robust inference infrastructure.