併發請求下的 Prefill 與 Decode：優化 LLM 推論效能的關鍵技術
Original: Prefill and Decode for Concurrent Requests - Optimizing LLM Performance
When deploying large language models (LLMs), maintaining low latency and high throughput under high concurrency (concurrent requests) is…
LLM 推論包含計算密集的 Prefill（處理輸入）與記憶體頻寬受限的 Decode（逐字生成）階段。當面對多個併發請求時，傳統靜態批處理會導致資源浪費。本文介紹了連續批處理（Continuous Batching）、區塊預填充（Chunked Prefill）以及 Prefill-Decode 分離等技術，幫助開發者在高併發場景下最大化吞吐量並降低延遲。
When deploying large language models (LLMs), maintaining low latency and high throughput under high concurrency (concurrent requests) is one of the greatest challenges AI engineers face today. This technical article from the Hugging Face community (written by TNG Technology Consulting) provides an in-depth analysis of the two core stages of LLM inference — Prefill and Decode — and explores the key techniques for optimizing these two stages.
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