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性能干扰和可预测性 |\n| 专属资源池 | 高风险或高保障服务 | 容量利用与隔离成本 |\n\n具体MIG能力因GPU型号、驱动和软件栈而异，本文不列未经核验的实例规格。\n\n## 切分前必须回答六个问题\n\n1. 当前GPU型号与软件版本是否支持目标切分方式；\n2. 模型显存、计算和带宽需求是多少；\n3. 工作负载能否接受共享物理设备；\n4. 性能与故障边界是否经过压力测试；\n5. 监控、计量和故障定位能否识别切分实例；\n6. 服务扩缩容、升级和回收如何执行。\n\n只看平均显存占用不足以决定切分规格。\n\n## 把切分实例纳入资源池\n\n资源模型可以是：\n\n`物理GPU → 切分配置 → 可分配实例 → 标准规格 → 项目配额 → 模型服务`\n\n每个标准规格应描述显存、适用任务、服务等级、最大时长、计量单位和已知限制。应用申请规格，不直接修改底层切分配置。\n\n## 切分配置如何进入生命周期\n\n- 规划：依据任务画像设计候选规格；\n- 验证：测试性能、稳定性和故障影响；\n- 发布：加入资源目录并定义配额；\n- 分配：由调度根据兼容与容量选择；\n- 监控：关联物理设备、实例和服务；\n- 变更：维护窗口内调整配置；\n- 回收：释放实例并核对残留任务；\n- 复盘：比较实际使用和服务产出。\n\n切分变更可能影响运行任务，必须根据产品和驱动机制设计维护流程。\n\n## 如何衡量切分是否有效\n\n不要只看GPU利用率。还应观察：\n\n- 规格申请等待时间；\n- 任务成功率和尾部延迟；\n- 显存不足与被驱逐情况；\n- 物理GPU与切分实例使用；\n- 单位成功任务成本；\n- 运维事件和配置变更成本。\n\n如果资源效率提高但服务质量下降，切分策略并不成功。\n\n\n\n## FAQ\n\n### MIG是否等于GPU虚拟化？\n\nMIG是一类硬件支持的资源切分机制，但企业还可能采用其他虚拟化或共享方式。具体能力边界需看硬件和软件栈。\n\n### 所有NVIDIA GPU都支持MIG吗？\n\n不是。支持范围及规格会变化，应以目标硬件与当前官方技术文档为准，并完成本地验证。\n\n### MIG一定比整卡利用率高吗？\n\n不一定。任务规格不匹配、切分过碎或容量波动都可能产生新的空闲和运维成本。\n\n### MIG适合训练大模型吗？\n\n取决于模型规模、显存、互联和性能要求。大型训练通常更倾向整卡或多卡资源，但必须以实际评测为准。\n\n## 下一步\n\n选择两类典型任务，分别测试整卡与候选切分规格，比较质量、延迟、稳定性、等待时间和单位成本后再发布规格。\n","\u003Cdiv class=\"github-markdown-body\">\u003Cp>NVIDIA MIG等GPU资源切分机制适合把支持的物理GPU划分为更细粒度、具有明确资源边界的可分配单元。企业资源池不应为了提高表面利用率而全面切分，而应根据硬件支持、工作负载显存、性能隔离、生命周期和运维复杂度，决定独占、切分或共享。\u003C\u002Fp>\n\u003Ch2>资源切分解决什么问题\u003C\u002Fh2>\n\u003Cp>完整GPU对轻量推理或开发任务可能过大，静态独占会产生空闲。资源切分可以帮助：\u003C\u002Fp>\n\u003Cul>\n\u003Cli>提供更小的标准规格；\u003C\u002Fli>\n\u003Cli>让多个兼容任务共享物理设备；\u003C\u002Fli>\n\u003Cli>明确分配边界和配额；\u003C\u002Fli>\n\u003Cli>提高资源池调度粒度；\u003C\u002Fli>\n\u003Cli>将设备能力产品化为服务规格。\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>但切分不会自动提高模型吞吐，也不适合所有任务。\u003C\u002Fp>\n\u003Ch2>MIG、时间共享和整卡独占如何选择\u003C\u002Fh2>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>方式\u003C\u002Fth>\n\u003Cth>适用倾向\u003C\u002Fth>\n\u003Cth>主要考虑\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd>整卡独占\u003C\u002Ftd>\n\u003Ctd>大模型、关键任务、强性能要求\u003C\u002Ftd>\n\u003Ctd>成本较高，但边界清晰\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>MIG切分\u003C\u002Ftd>\n\u003Ctd>支持硬件上的中小型稳定任务\u003C\u002Ftd>\n\u003Ctd>需验证规格、显存和兼容性\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>时间共享\u003C\u002Ftd>\n\u003Ctd>开发、测试或可接受波动任务\u003C\u002Ftd>\n\u003Ctd>性能干扰和可预测性\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>专属资源池\u003C\u002Ftd>\n\u003Ctd>高风险或高保障服务\u003C\u002Ftd>\n\u003Ctd>容量利用与隔离成本\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003Cp>具体MIG能力因GPU型号、驱动和软件栈而异，本文不列未经核验的实例规格。\u003C\u002Fp>\n\u003Ch2>切分前必须回答六个问题\u003C\u002Fh2>\n\u003Col>\n\u003Cli>当前GPU型号与软件版本是否支持目标切分方式；\u003C\u002Fli>\n\u003Cli>模型显存、计算和带宽需求是多少；\u003C\u002Fli>\n\u003Cli>工作负载能否接受共享物理设备；\u003C\u002Fli>\n\u003Cli>性能与故障边界是否经过压力测试；\u003C\u002Fli>\n\u003Cli>监控、计量和故障定位能否识别切分实例；\u003C\u002Fli>\n\u003Cli>服务扩缩容、升级和回收如何执行。\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cp>只看平均显存占用不足以决定切分规格。\u003C\u002Fp>\n\u003Ch2>把切分实例纳入资源池\u003C\u002Fh2>\n\u003Cp>资源模型可以是：\u003C\u002Fp>\n\u003Cp>\u003Ccode>物理GPU → 切分配置 → 可分配实例 → 标准规格 → 项目配额 → 模型服务\u003C\u002Fcode>\u003C\u002Fp>\n\u003Cp>每个标准规格应描述显存、适用任务、服务等级、最大时长、计量单位和已知限制。应用申请规格，不直接修改底层切分配置。\u003C\u002Fp>\n\u003Ch2>切分配置如何进入生命周期\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>规划：依据任务画像设计候选规格；\u003C\u002Fli>\n\u003Cli>验证：测试性能、稳定性和故障影响；\u003C\u002Fli>\n\u003Cli>发布：加入资源目录并定义配额；\u003C\u002Fli>\n\u003Cli>分配：由调度根据兼容与容量选择；\u003C\u002Fli>\n\u003Cli>监控：关联物理设备、实例和服务；\u003C\u002Fli>\n\u003Cli>变更：维护窗口内调整配置；\u003C\u002Fli>\n\u003Cli>回收：释放实例并核对残留任务；\u003C\u002Fli>\n\u003Cli>复盘：比较实际使用和服务产出。\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>切分变更可能影响运行任务，必须根据产品和驱动机制设计维护流程。\u003C\u002Fp>\n\u003Ch2>如何衡量切分是否有效\u003C\u002Fh2>\n\u003Cp>不要只看GPU利用率。还应观察：\u003C\u002Fp>\n\u003Cul>\n\u003Cli>规格申请等待时间；\u003C\u002Fli>\n\u003Cli>任务成功率和尾部延迟；\u003C\u002Fli>\n\u003Cli>显存不足与被驱逐情况；\u003C\u002Fli>\n\u003Cli>物理GPU与切分实例使用；\u003C\u002Fli>\n\u003Cli>单位成功任务成本；\u003C\u002Fli>\n\u003Cli>运维事件和配置变更成本。\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>如果资源效率提高但服务质量下降，切分策略并不成功。\u003C\u002Fp>\n\u003Ch2>FAQ\u003C\u002Fh2>\n\u003Ch3>MIG是否等于GPU虚拟化？\u003C\u002Fh3>\n\u003Cp>MIG是一类硬件支持的资源切分机制，但企业还可能采用其他虚拟化或共享方式。具体能力边界需看硬件和软件栈。\u003C\u002Fp>\n\u003Ch3>所有NVIDIA GPU都支持MIG吗？\u003C\u002Fh3>\n\u003Cp>不是。支持范围及规格会变化，应以目标硬件与当前官方技术文档为准，并完成本地验证。\u003C\u002Fp>\n\u003Ch3>MIG一定比整卡利用率高吗？\u003C\u002Fh3>\n\u003Cp>不一定。任务规格不匹配、切分过碎或容量波动都可能产生新的空闲和运维成本。\u003C\u002Fp>\n\u003Ch3>MIG适合训练大模型吗？\u003C\u002Fh3>\n\u003Cp>取决于模型规模、显存、互联和性能要求。大型训练通常更倾向整卡或多卡资源，但必须以实际评测为准。\u003C\u002Fp>\n\u003Ch2>下一步\u003C\u002Fh2>\n\u003Cp>选择两类典型任务，分别测试整卡与候选切分规格，比较质量、延迟、稳定性、等待时间和单位成本后再发布规格。\u003C\u002Fp>\n\u003C\u002Fdiv>","markdown","NVIDIA MIG可将支持的GPU划分为更细粒度资源。本文比较整卡、MIG和共享方式，并说明规格设计、生命周期、计量和验证方法。","\u002Ffile\u002F2\u002F2026\u002F8\u002F887e34075e09.png?i=9240&name=nvidia-mig-gpu-resource-slicing.png","nvidia-mig-gpu-resource-slicing","NVIDIA MIG与GPU资源切分：企业资源池应该怎样使用？","NVIDIA MIG资源切分,GPU资源切分,MIG GPU","NVIDIA MIG与GPU资源切分：企业资源池如何使用？- AGIOne","NVIDIA MIG资源切分,GPU资源切分,MIG GPU,GPU切分实例,GPU资源池,GPU虚拟化","2026-08-26","2026-09-01 16:27:41","2026-08-26 18:05:00","AGIOne","\u002Ffile\u002F2\u002F2026\u002F6\u002F291951eaf479.png?i=9162&name=AGIOne_反白.png","最佳实践",1788322575506,{"data":236,"status":23,"timestamp":250},{"previous":237,"next":244},{"id":238,"title":239,"cover":240,"slug":241,"productName":231,"blogTypeName":242,"blogTypeCode":243},209,"Hybrid AI 是什么？公有模型、私有模型与本地算力如何协同","\u002Ffile\u002F2\u002F2026\u002F8\u002F3abea5c50832.png?i=9225&name=what-is-hybrid-ai-public-private-models-local-compute.png","what-is-hybrid-ai-public-private-models-local-compute","行业科普","popular-science",{"id":245,"title":246,"cover":247,"slug":248,"productName":231,"blogTypeName":233,"blogTypeCode":249},212,"多租户AI平台如何兼顾隔离与资源效率？","\u002Ffile\u002F2\u002F2026\u002F8\u002F6146abf4ddd7.png?i=9237&name=multitenant-ai-platform-isolation-efficiency.png","multitenant-ai-platform-isolation-efficiency","best-practice",1788322575546,{"data":252,"status":23,"timestamp":341},[253,269,285,299,312,328],{"tenantId":5,"id":254,"title":255,"desc":256,"intro":257,"industryId":258,"slug":259,"productId":51,"languageId":6,"tags":260,"siteId":6,"cover":261,"coverBig":262,"seoTitle":263,"keywords":263,"description":264,"pdfurl":265,"isTop":22,"status":6,"publishedAt":10,"contentUpdatedAt":10,"sortIndex":10,"platform":10,"createdAt":266,"updatedAt":267,"languageName":16,"productName":231,"productIcon":232,"productCode":231,"industryName":268},59,"墨西哥医疗机构：AGIOne统一AI效能层驱动GPU利用率增长","墨西哥一家国家级医疗机构面临GPU资源碎片化、利用率不足20%的困境。通过部署AGIOne统一AI效能层，实现异构算力池化、多模型统一服务与混合云弹性调度。GPU利用率实现翻倍以上增长，新AI服务部署周期从\"月\"压缩到\"天\"，成功将AI从成本项目转化为支撑核心诊断业务的战略能力。","背景：该墨西哥医疗机构率先部署多个AI试点项目用于影像诊断、放射分析及患者服务，但各应用独占硬件导致GPU利用率低于20%，扩容依赖采购、CapEx持续飙升，AI规模化陷入瓶颈。\n\n方案：采用AGIOne构建统一AI效能层，实现四大突破：GPU资源池化整合碎片化算力、异构纳管跨型号GPU统一调度、多模型聚合实现LLM统一抽象服务、混合云弹性架构本地资源触顶时自动扩展至云端。同步引入全生命周期运营方法论，基于延迟、成本、数据敏感度三维智能路由自动决策工作负载去向。\n\n收益：GPU利用率从不足20%实现翻倍以上增长，硬件投资回报率显著改善；新AI服务上线及运营变更周期从\"月\"压缩至\"天\"；混合云弹性架构使扩容不再受制于硬件采购，业务全程不中断；集中化控制台强化数据治理与合规管控，AI从成本负担转变为支撑核心诊断业务的战略护城河。\n\n",5,"mexico_healthcare_gpu_utilization_ai","异构GPU资源池化$$GPU利用率翻倍增长,混合云弹性调度$$本地云端无缝扩展,多模型统一治理$$异构LLM聚合智能路由","\u002Ffile\u002F2\u002F2026\u002F6\u002F9b010d5fc872.png?i=7884&name=预览图.png","\u002Ffile\u002F2\u002F2026\u002F6\u002F236f21262dc6.png?i=7885&name=官网Bannner.png","医疗AI，AI效能层，GPU资源调度，模型聚合平台，混合云AI，智能路由，AGIOne，异构算力纳管，AI规模化运营，医疗影像诊断，AI生命周期管理，企业AI基础设施，智算资源调度，AI运营平台，算力池化","墨西哥国家级医疗机构在推进多个AI试点项目后，遭遇GPU资源碎片化、利用率低于20%、扩容严重依赖硬件采购等困局。该机构采用AGIOne统一AI效能层解决方案，通过资源池化、异构纳管、模型聚合与混合云弹性四大能力，构建全新的AI运营基础设施。项目落地后GPU利用率实现翻倍以上增长，新AI服务部署周期从\"月\"压缩到\"天\"，为医疗行业AI规模化运营提供了标杆实践。","\u002Ffile\u002Fcase\u002F59\u002F墨西哥医疗机构：AGIOne统一AI效能层驱动GPU利用率增长.pdf","2026-06-16 12:15:15","2026-08-03 08:54:02","卫生医疗",{"tenantId":5,"id":5,"title":270,"desc":271,"intro":272,"industryId":6,"slug":273,"productId":48,"languageId":6,"tags":274,"siteId":6,"cover":275,"coverBig":276,"seoTitle":10,"keywords":277,"description":278,"pdfurl":279,"isTop":22,"status":6,"publishedAt":10,"contentUpdatedAt":10,"sortIndex":10,"platform":6,"createdAt":280,"updatedAt":281,"languageName":16,"productName":42,"productIcon":282,"productCode":283,"industryName":284},"海通证券：云管集成迁移模块，实现异构平台自助迁移","作为中国综合性证券机构中的龙头企业，海通证券主动拥抱金融科技的发展浪潮，率先在行业内启动金融云的建设。2017年，海通金融云的建设进程进一步清晰。结合自身的业务需求，海通证券将金融云建设目标确立为实现传统基础设施向混合云架构转型。","挑战：海通证券构建金融云平台后，上千租户存在数据流转问题，需实现业务系统在不同资源池的灵活迁移。\n方案：以 REST API 接口将迁移服务集成到云管平台，提供自助迁移模块；通过云平台 API 自动调用对接多云；采用极简向导式操作界面，6 步完成迁移。\n收益：赋能云管平台用户，降低操作和管理难度；实现用户自助迁移，数据流转更自由，提升平台竞争力。","hypermotion-case-studies-haitong-security","云管平台集成$$内嵌云迁移模块,混合云迁移$$多个资源池数据迁移,自主式迁移$$企业客户自助执行迁移任务","\u002Ffile\u002F2\u002F2024\u002F11\u002Fa4ef4a1f6e63.jpg?i=110&name=europe-1911782_1280.jpg","\u002Ffile\u002F2\u002F2025\u002F4\u002F1869d0643915.png?i=8674&name=迁移金融.png","金融业,金融数据迁移,自动式迁移,可视化迁移,VMware迁移,热迁移,多云兼容,海通证券,HyperMotion云迁移","海通证券将HyperMotion云迁移模块集成至金融云平台，实现上千租户的自助式系统迁移。​用户可通过6步可视化操作，轻松完成VMware虚拟机、物理机等资源的迁移，无需停机，确保业务连续性。​该方案支持多云对接，提升数据资源流转效率，增强平台竞争力。","\u002Ffile\u002Fcase\u002F2\u002F海通证券：云管集成迁移模块，实现异构平台自助迁移.pdf","2021-12-13 14:51:31","2026-08-03 16:26:43","\u002Ffile\u002F2\u002F2025\u002F4\u002Fe4aee932c781.png?i=8652&name=云迁移横版文字反白.png","hypermotion","金融",{"tenantId":5,"id":119,"title":286,"desc":287,"intro":288,"industryId":48,"slug":289,"productId":48,"languageId":6,"tags":290,"siteId":6,"cover":291,"coverBig":292,"seoTitle":10,"keywords":293,"description":294,"pdfurl":295,"isTop":22,"status":6,"publishedAt":10,"contentUpdatedAt":10,"sortIndex":10,"platform":6,"createdAt":296,"updatedAt":297,"languageName":16,"productName":42,"productIcon":282,"productCode":283,"industryName":298},"云南某市：政务云高效热迁移，一次成功","为了进一步提高政务服务效率与质量，加强信息资源的整合共享，同时也为了进一步提高成本效益，该市政务云需要尽快将全部政务系统从某私有云平台迁到天翼云上。","挑战：原环境依赖VMware，难以适配信创平台，国产化替代存在业务中断与系统兼容风险。\n方案：通过HyperMotion完成VMware向国产鲲鹏平台的迁移，支持多类型云主机一键转换与部署。\n收益：政务系统平滑切换至国产云，保障关键服务持续运行，全面支撑国产信创战略落地。\n\n","yunnan-government-system-migration","跨平台$$私有云平台切换,跨部门$$30个部门+,25天$$迁移周期成功缩短1个月","\u002Ffile\u002F2\u002F2025\u002F4\u002F4f17d1cb870c.png?i=8697&name=云南.png","\u002Ffile\u002F2\u002F2025\u002F4\u002F7a65b48192c0.png?i=8660&name=云南.png","政务云, 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地址。\n收益：降低人力投入，依业务单位需求阶段性迁移 180 台主机，降低成本；运维人员易上手，技术门槛低。","hypermotion-case-studies-government-msp","180台主机$$从Xen到华为HCS,33套业务系统$$需协调多业务单位时间,1个交付人员$$自助式渐进迁移","\u002Ffile\u002F2\u002F2024\u002F11\u002Fb4d29853bc5d.jpg?i=116&name=bus-2069419_1280.jpg","\u002Ffile\u002F2\u002F2025\u002F4\u002F9bc27e3628a1.png?i=8670&name=政府.png","政务云迁移, 华为云, Xen虚拟化, KVM虚拟化, HyperMotion云迁移, 整机热迁移, 自动化迁移, IP地址指定, 运维技术门槛, 分布式迁移, 多部门业务协调","政务云服务商采用HyperMotion云迁移工具，将180台主机、33套业务系统从Xen虚拟化资源池迁移至华为云KVM虚拟化资源池。​采用整机热迁移、块级差量复制和API智能适配，实现业务系统无中断迁移，显著降低人力投入，提升迁移效率。","\u002Ffile\u002Fcase\u002F4\u002F政务云服务商：跨平台虚机迁移，分批多次效率高.pdf","2021-12-13 14:53:37","2026-08-02 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Stack平台替代VMware环境，计划2年内完成全省超过30家分行、约1万台虚拟机的自主迁移。采用HyperMotion工具，IT团队分批推进，高效实现跨平台云化，构建统一、安全、可控的金融云基础架构。","\u002Ffile\u002Fcase\u002F50\u002F浙江农信：VMware替代，迁移万台虚机，Apasara Stack成核心承载平台.pdf","2025-07-01 16:24:40","2026-07-31 16:34:23",1788322575585,{"data":343,"status":23,"timestamp":437},[344,352,362,369,379,388,394,402,408,414,420,426],{"id":345,"title":346,"cover":347,"desc":348,"siteId":6,"slug":349,"typeId":217,"productId":51,"languageId":6,"status":6,"date":350,"createdAt":351,"productName":231,"productCode":231,"productIcon":232,"blogTypeName":233,"blogTypeCode":249,"languageName":16},207,"私有化 AI 平台适合哪些企业？6 个判断条件","\u002Ffile\u002F2\u002F2026\u002F8\u002Fa9d9c3b88741.png?i=9219&name=private-ai-platform-fit-assessment.png","从数据边界、本地算力、模型治理、多组织权限、运行要求和长期运营能力六个方面，判断企业是否适合建设私有化 AI 平台，并比较私有化、云服务与混合部署。","private-ai-platform-fit-assessment","2026-08-25","2026-08-25 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组织模型服务、授权、路由和计量，并以公共治理域连接账户、权限、额度、账单与结算，帮助企业将分散的 AI 试点转化为可治理的平台能力。","what_is_agione_enterprise_ai_platform","2026-08-04","2026-08-04 17:33:37",{"id":403,"title":404,"cover":405,"desc":406,"siteId":6,"slug":407,"typeId":217,"productId":51,"languageId":6,"status":6,"date":350,"createdAt":368,"productName":231,"productCode":231,"productIcon":232,"blogTypeName":233,"blogTypeCode":249,"languageName":16},195,"企业级 AI 平台怎么建设？从业务目标到生产运行的完整框架","\u002Ffile\u002F2\u002F2026\u002F8\u002Ffdca41ae2ee3.png?i=9226&name=enterprise-ai-platform-construction-framework.png","企业级 AI 平台建设应从业务目标出发，连接模型服务、异构算力、权限治理、Token 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