長 Prompt 如何阻塞其他請求？優化 LLM 推理效能與解決隊頭阻塞的關鍵策略
Original: How Long Prompts Block Other Requests - Optimizing LLM Performance
As the context windows of large language models (LLMs) continue to expand — from the early 4k and 8k, to the now-common 32k and even 128k…
本文探討 LLM 在處理長 Prompt 時，因 Prefill（預填充）階段佔用大量 GPU 運算，導致其他短請求或生成階段被阻塞的「隊頭阻塞」現象。文章深入分析了 Prefill 與 Decode 階段的資源衝突，並提出分塊預填充（Chunked Prefill）與 Prompt 快取（Prompt Caching）等關鍵優化策略，以在多用戶併發環境下顯著降低延遲並提升吞吐量。
As the context windows of large language models (LLMs) continue to expand — from the early 4k and 8k, to the now-common 32k and even 128k or more — users have begun submitting extremely long prompts, such as entire academic papers, complete codebases, or massive RAG-retrieved documents. However, this trend toward "long prompts" has introduced serious performance challenges for LLM inference serving, with the most critical issue being **Head-of-Line (HoL) Blocking**.
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