解鎖連續批次處理（Continuous Batching）中的非同步機制
Original: Unlocking asynchronicity in continuous batching
As the demand for deploying large language models (LLMs) in production environments surges, how to improve inference efficiency and reduce…
本文介紹了 Hugging Face 在 LLM 推論優化上的最新技術：在連續批次處理（Continuous Batching）中解鎖非同步（Asynchronicity）機制。傳統的連續批次處理在排程、GPU 執行與 Token 處理間存在同步瓶頸。透過將這些步驟非同步化，能有效重疊 CPU 與 GPU 的工作負載，進而大幅提升推論吞吐量並優化首字輸出時間（TTFT）。
As the demand for deploying large language models (LLMs) in production environments surges, how to improve inference efficiency and reduce costs has become a critical issue. Hugging Face has published a technical article that delves into the key technology for unlocking "asynchronicity" within "continuous batching."
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