解鎖更長的文本生成：深入探討 Key-Value (KV) 快取量化技術
Original: Unlocking Longer Generation with Key-Value Cache Quantization
During the inference process of large language models (LLMs), the self-attention mechanism needs to store the Key and Value vectors of…
隨著 LLM 上下文長度增加，KV Cache 佔用的記憶體成為推論瓶頸。Hugging Face 探討了 KV Cache 量化技術（如 INT8 和 INT4），這項技術能減少高達 75% 的快取記憶體佔用。這不僅能顯著提升推論的批次大小（Batch Size），還能在不犧牲太多精度的情況下，讓消費級顯卡也能運行超長文本生成。
During the inference process of large language models (LLMs), the self-attention mechanism needs to store the Key and Value vectors of historical tokens (i.e., the KV Cache) to avoid redundant computation. However, as the context length grows, the memory requirements of the KV Cache increase linearly — sometimes even exceeding the model weights themselves — making it the primary bottleneck limiting long-text generation.
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