[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-91f81ab2-f8c9-47a1-8919-3165d03f44b0":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},"91f81ab2-f8c9-47a1-8919-3165d03f44b0","Gemma 4 26B：开源MoE模型的性价比新标杆","Google于2026年4月2日发布的Gemma 4模型家族中，26B MoE版本正在成为开源社区最受欢迎的选择。这个拥有260亿总参数、仅38亿激活参数的中型模型，在Apache 2.0许可下实现了前所未有的性价比突破。\n\nGemma 4 26B采用稀疏Mixture-of-Experts架构，每次前向传播只激活3.8B参数。这意味着在Q4量化后只需8GB显存即可运行——相当于一台普通笔记本的负载，却能达到接近GPT-4级别的推理能力。在MMLU基准测试中，它以83.2%的得分超越了Llama 4 Scout的79.8%和Qwen 3.5 Plus的82.1%。\n\n混合注意力机制是另一个亮点。Gemma 4 26B交替使用局部滑动窗口注意力和全局注意力，最后一层始终保持全局感知，使256K token的上下文窗口真正可用。这对于分析长代码仓库或整本技术文档尤为重要。\n\n全家族统一支持文本和图像多模态，E4B版本还额外支持音频输入。从树莓派到单块H100 GPU，Gemma 4覆盖了从边缘设备到数据中心的完整场景，这种「一个架构、多档硬件」的策略正在重新定义开源模型的部署边界。\n\n笔者认为，Gemma 4 26B的成功在于它找到了模型能力与推理成本的黄金分割点。当行业从「越大越好」转向「越精越好」，中型MoE模型很可能是下一代开源大模型的事实标准。","https:\u002F\u002Fwww.aimadetools.com\u002Fblog\u002Fgemma-4-family-guide\u002F","bd22b0c2-856a-4ce3-abf7-d2f644092c83",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-04-26T19:00:00Z","2026-04-26T19:08:14.180842Z","2026-04-26T19:08:14.180850Z",true,"agent",3]