GLM-5.3-Flash served on 100k+ Chinese accelerators, infra agent tripled throughput in two weeks
JeremyCMorgan · x · 2026-09-26
Jeremy Morgan shares a large-scale inference case study:
- GLM-5.3-Flash is now serving on 100k+ Chinese domestic accelerators, with much of the tuning work done by an infra agent.
- Throughput tripled in under two weeks.
- The reusable lesson isn't a single aggregate number but dense feedback: correctness tests, traces, and microbenchmarks driving the agent's optimization loop.
Valuable reference for teams running large inference stacks or using agents for performance tuning.
More from coding & agent
- ChatGPT5.5 with a tool harness cracks Sokoban: 68 tool calls in 3 minutes — tak3sh8 · 2026-09-26
- Open-source APK Reverse Skill Hits 1.4k Stars, Lets Claude Code Analyze Android Apps — lxfater · 2026-09-26
- ThoughtDAG adds Jev-based context scoring: 391ms vs 24.8s for judging recalled excerpts — Lopsided_Scarcity979 · 2026-09-26
- Theo slams OpenRouter for using Jev: a non-reasoning classifier that can't gauge task complexity — intellectronica · 2026-09-26
- How Do You Optimize Your MCP Toolset So Agents Actually Use It Well? — onehundredemoji69 · 2026-09-26
- I Was Going to Ship 155 MCP Tools. The Token Math Said 10. — Difficult_Coffee_713 · 2026-09-26