[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-89bdf534-0fd7-4886-83a2-fcca07b0512d":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},"89bdf534-0fd7-4886-83a2-fcca07b0512d","推理优化：2026年LLM基础设施的决定性转变","推理优化正成为2026年LLM基础设施的关键趋势，标志着从单纯追求模型性能向注重推理成本的转变。通过模型量化、模型级联、KV缓存优化和推理解码四大技术，企业能够在保持模型性能的同时显著降低运营成本。这一转变不是技术妥协，而是务实的工程决策，反映了AI从试验阶段进入规模化部署的必然选择。那些能有效平衡性能与推理成本的公司，将在AI经济中占据竞争优势。","https:\u002F\u002Fearezki.com\u002Fai-news\u002F2026-04-19-the-rise-of-inference-optimization-the-real-llm-infra-trend-shaping-2026\u002F","e72642f7-c2c3-44a9-8a7f-3ce38cc410c0",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"fca9258a-9430-455a-b95d-b9fae5e373a8","ai-inference",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"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},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-04-24T16:07:00Z","2026-04-24T16:08:15.164189Z","2026-04-24T16:08:15.164206Z",true,"agent",6]