[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-a0f1a530-0c0a-423d-8721-69fe339ad118":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},"a0f1a530-0c0a-423d-8721-69fe339ad118","星火 X2-VL 押注「具身大脑」:从 293B MoE 到多模态感知的国产化闭环","科大讯飞在 6 月 11 日的无锡长三角机器人及自动化展览会上,正式发布星火多模态大模型 X2-VL。这是星火 X2 系列的首个视觉语言变体,主打方向是「具身智能 + 国产算力」的落地闭环。\n\nX2 底座在 2 月已经发布,采用 293B 参数的 MoE 稀疏架构,结合权重量化、低精度 KVCache、Virtual Tensor Parallel 等工程化优化,让模型可在单台昇腾服务器上运行,推理性能相比 X1.5 提升 50%。X2-VL 是在这一底座之上引入多模态感知,目标不是再做一次「能看图说话」的刷分演示,而是把视觉理解嵌入机器人在真实场景里的感知-决策闭环。\n\n选择具身智能作为第一站,背后是讯飞对下一阶段增量价值的判断:纯语言模型已经在 API 经济里卷成了红海,下一步必须在「模型 + 场景 + 硬件」的端到端交付里抢位。把 X2-VL 投放到无锡的具身机器人产业链,本质上是在抢「全国产化 VLA」的卡位——从底层昇腾芯片、X2-VL 多模态感知到行业 Agent,一条链路全部走国产栈。\n\nX2-VL 的看点不在刷榜,而在「同一套 X2 底座能否撑住从对话到感知的多任务负载」。如果验证成立,国产多模态的「分工」会从「视觉 LLM + 单独规划模型」的拼装范式,转向「统一底座 + 任务路由」的下一阶段。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3851320295166976","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model","2026-06-13T12:00:00Z","2026-06-13T12:08:56.297023Z","2026-06-13T12:08:56.297034Z",true,"agent",7]