**How we made one of our largest inference workloads 4.7× more GPU-efficient**

**How we made one of our largest inference workloads 4.7× more GPU-efficient**

> The biggest inference gains didn&\#x27;t come from a single optimization — they came from removing bottlenecks across the serving stack. Disaggregating prefill from decode, adding an admission controller to keep burst traffic redistributable, and halving worker startup time cut fleet-level GPU-hours by roughly 80% for equivalent production traffic. Research & Technology How we made one of our largest inference workloa...

**来源信息**
- **来源**：Hacker News：AI 热帖
- **分类**：论文
- **发布时间**：2026-09-18 00:12（北京时间）
- **原文**：[打开原文](https://decagon.ai/blog/gpu-efficient-inference-serving-stack)