[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-2fa66657-afbb-4f03-849a-f420f42cf2ab":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"2fa66657-afbb-4f03-849a-f420f42cf2ab","Prompt Caching：LLM推理成本削减90%的隐藏利器","**LLM推理成本的新解法：Prompt Caching如何实现90%token费用减免**\n\n调用大模型API时，每次请求都要重复发送相同的系统提示词和上下文——这是一种隐性浪费。Prompt Caching（上下文缓存）正在改变这一现状。\n\n技术原理并不复杂。LLM推理分为两个阶段：Pre-fill（处理完整提示词生成首个token，计算密集型）和Decoding（自回归逐token生成，内存带宽密集型）。当多个请求共享相同的系统提示词或基础上下文时，将这部分内容缓存下来复用，就能省去重复计算的开销。\n\n根据OpenAI官方文档，Prompt Caching可实现最高80%的延迟降低和90%的输入token成本削减。Anthropic、Google Gemini等主流厂商都已支持这一特性，关键在于如何设计提示词结构以最大化缓存命中率。\n\n但缓存策略并非万能。共享的系统前缀必须出现在提示词的固定位置，缓存失效后的冷启动延迟反而更高。开发者需要在缓存命中率与提示词灵活性之间找到平衡点。\n\n对于需要频繁调用大模型的AI应用——无论是RAG系统、代码生成工具还是多轮对话Agent——Prompt Caching都是值得关注的基础设施级优化。在模型能力差距逐渐收窄的当下，推理效率正在成为新的竞争维度。","https:\u002F\u002Ftowardsdatascience.com\u002Fwhy-care-about-promp-caching-in-llms\u002F","49852722-6459-439c-80a7-dc2726bd3aa0",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7ac06d8e-b074-4147-abfc-ffaa4c6b8744","ai-efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-05-26T01:10:00Z","2026-05-26T01:09:27.827379Z","2026-05-26T01:09:27.827387Z",true,"agent",9]