GLM agent built its own inference infra in two weeks, tripling end-to-end throughput
jietang · x · 2026-09-17
Zhipu engineer jietang shares how GLM-5.3-Flash went from first run on domestic accelerators to serving all production traffic in two weeks, with 3.2× end-to-end throughput — with most optimization done by an Infra Agent powered by GLM-5.3 itself.
The constraints were tough: limited memory and interconnect bandwidth, 1M-token context, multimodal requests, and an immature stack with missing kernels. Key trade-offs: compute for memory (ReplaySSM), communication for memory (intra-node tensor parallelism), precision for capacity (mixed INT8/FP8/BF16 caching), and disaggregation for scheduling freedom (Encode–Prefill–Decode).
The most important lesson wasn't any single optimization but dense feedback: local correctness tests, execution traces, microbenchmarks, and end-to-end measurements enabling targeted hypothesis testing instead of relying on aggregate metrics alone.
More from coding & agent
- Dev builds 3D universe explorer with 20 galaxies, 767 planets using Codex GPT-6.1 Sol — OpenAIDevs · 2026-11-02
- ChatGPT, Claude and Perplexity's fetchers don't run JavaScript, leaving client-rendered sites invisible to AI — gaganghotra_ · 2026-11-02
- SGLang boosts multimodal inference >10% by replacing GPU memory bookkeeping with a dict lookup — charles_irl · 2026-11-02
- kivi: hold Fn, talk, and it types polished text anywhere on your Mac — itsOmSarraf_ · 2026-11-02
- A base LLM plus 11 lines of code is all you need for a decision model — helloiamleonie · 2026-11-02
- Fix codebase structure before asking AI agents to write tests, says engineer — arpit_bhayani · 2026-11-02