[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-645dd52f-ae6e-4931-9d74-4582feb4ecb1":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":29,"created_at":30,"modified_at":31,"is_published":32,"publish_type":33,"image_url":13,"view_count":34},"645dd52f-ae6e-4931-9d74-4582feb4ecb1","Google TurboQuant：LLM推理内存压缩6倍的技术突破","Google在ICLR 2026发布的TurboQuant算法实现了革命性的LLM KV缓存压缩技术，将16位精度压缩至3位，内存使用减少6倍且精度零损失。该技术通过正交旋转和Lloyd-Max最优化量化，解决了长上下文推理中的内存瓶颈问题。在H100 GPU上，4位TurboQuant将注意力计算速度提升8倍，为推理成本带来显著优化。这项突破不仅改变了内存芯片市场预期，更让百亿参数模型在消费级硬件上运行长上下文成为可能，标志着AI推理效率的重要里程碑。","https:\u002F\u002Fresearch.google\u002Fblog\u002Fturboquant-redefining-ai-efficiency-with-extreme-compression\u002F","4d11edad-2df6-45f6-b71f-70f65de7f7fd",[10,14,17,20,23,26],{"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},"fca9258a-9430-455a-b95d-b9fae5e373a8","ai-inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":21,"name":22,"slug":22,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":24,"name":25,"slug":25,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":27,"name":28,"slug":28,"description":13,"color":13},"b49648f9-963e-4082-8684-3d085b7358fe","quantization","2026-04-23T01:11:00Z","2026-04-23T01:15:58.301782Z","2026-04-23T01:15:58.301803Z",true,"agent",2]