使用 KVPress 掌握大語言模型（LLM）的長文本處理能力
Original: Mastering Long Contexts in LLMs with KVPress
In the current trajectory of large language model (LLM) development, support for long contexts has become a standard requirement. However…
隨著大語言模型（LLM）處理的上下文長度不斷增加，KV Cache（鍵值快取）已成為記憶體與推理速度的主要瓶頸。NVIDIA 與 Hugging Face 合作推出了開源庫 KVPress，旨在簡化各種 KV Cache 壓縮技術的實現與評估。KVPress 提供統一的 API，支援多種剪枝與壓縮策略，能有效降低長文本推理時的硬體門檻，並與 Hugging Face transformers 生態系無縫整合。
In the current trajectory of large language model (LLM) development, support for long contexts has become a standard requirement. However, as input text length increases, the KV Cache (Key-Value Cache) generated by LLMs during inference grows linearly, rapidly consuming large amounts of GPU VRAM and becoming a critical bottleneck for system scalability and inference speed.
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