GLM-5.3 自建推理基础设施,吞吐量提升 3 倍

Z.ai · @Zai_org · X·2026-09-17 15:05·1天前
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智谱分享 GLM-5.3 如何帮助构建并优化服务 GLM-5.3-Flash 的推理基础设施,系统从首次成功运行到生产就绪用时不到两周,端到端吞吐量相对初始基线提升 3 倍。关键在于密集反馈:本地正确性测试、执行轨迹、微基准和端到端测量,使团队能进行有针对性的假设验证,而非仅依赖聚合性能指标。

Z.ai@Zai_org
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GLM-5.3 自建推理基础设施,吞吐量提升 3 倍

2026-09-17 15:05· 1天前
AI 导读

智谱分享 GLM-5.3 如何帮助构建并优化服务 GLM-5.3-Flash 的推理基础设施,系统从首次成功运行到生产就绪用时不到两周,端到端吞吐量相对初始基线提升 3 倍。关键在于密集反馈:本地正确性测试、执行轨迹、微基准和端到端测量,使团队能进行有针对性的假设验证,而非仅依赖聚合性能指标。

We’re sharing how GLM-5.3 helped build and optimize the inference infrastructure serving GLM-5.3-Flash.

The system went from its first successful run to production readiness in less than two weeks, with end-to-end throughput tripling relative to the initial baseline.

The key was dense feedback: local correctness tests, execution traces, microbenchmarks, and end-to-end measurements that enabled targeted hypothesis testing rather than reliance on aggregate performance metrics alone.

https://z.ai/blog/glm-built-its-inference-infrastructure