在生產環境中優化你的大語言模型 (LLM) — Hugging Face 實戰指南
Original: Optimizing your LLM in production
This technical guide from Hugging Face systematically introduces the core strategies for deploying and optimizing large language models…
本指南深入探討如何在實際生產環境中優化大語言模型（LLM）的部署。內容涵蓋降低顯示記憶體（vRAM）佔用的關鍵技術，如 KV 快取、4-bit/8-bit 量化（GPTQ、AWQ）與 FlashAttention；並介紹提升推論吞吐量的進階方法，包括連續批次處理（Continuous Batching）、投機解碼（Speculative Decoding）以及多 GPU 分散式推論。這是一份針對開發者將開源模型落地的必讀實戰手冊。
This technical guide from Hugging Face systematically introduces the core strategies for deploying and optimizing large language models (LLMs) in production environments, aiming to address the two major pain points of LLM inference: high latency and high VRAM consumption.
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