NVIDIA褰掓。 - 鐟炴妧绉戞妧 //51269984.cn/tag/nvidia 一站式IT解决Ҏ提供?/description> Wed, 26 Mar 2025 06:31:14 +0000 zh-Hans hourly 1 //wordpress.org/?v=6.8.3 //51269984.cn/wp-content/uploads/2023/06/cropped-fav-32x32.png NVIDIA褰掓。 - 鐟炴妧绉戞妧 //51269984.cn/tag/nvidia 32 32 NVIDIA褰掓。 - 鐟炴妧绉戞妧 //51269984.cn/bytebridge-blog/weka-nvidia-blackwell-collaboration.html?utm_source=rss&utm_medium=rss&utm_campaign=weka-nvidia-blackwell-collaboration Tue, 25 Mar 2025 08:43:21 +0000 //51269984.cn/?p=27464 NVIDIA GB200QBlackwellQ^台正以前所未有的速度、规模和效率重塑 AI 计算。NVIDIA […]

WEKA 携手 NVIDIA BlackwellQ释?AI 推理无限潜能最先出现在瑞技U技?/p> ]]>

NVIDIA GB200QBlackwellQ^台正以前所未有?strong>速度、规模和效率重塑 AI 计算。NVIDIA Blackwell 专ؓ满下一?AI 工作负蝲的巨大需求而设计，在推理模型、AI 代理和 Token 生成{方面展CH破性进展。凭借其先进的架构，Blackwell GPU 提供高速通信、巨大的内存带宽以及无与伦比的计能力，q些都是实现实时 AI 决策所必需的。随着 AI 从单U的训练转向复杂的推理和决策Q基设施必须不断q化以跟上步伐?/p>

新型 AI 芯片
新型 AI 芯片
Blackwell 架构 GPU 拥有2080亿个晶体，采用专门定制的台U电 4NP 工艺刉。所?Blackwell 产品均采用双倍光L限尺寸的裸片Q通过 10 TB/s 的片间互联技术连接成一块统一?GPU?/span>

然而，仅靠强大?GPU 性能q远q不够?/p>

 

要充分释?AI 推理的潜力，云服务商q需要同样高性能的数据基设施Q以消除瓉q最大化资源利用率。这也是 WEKA 获得 NVIDIA GB200 高性能数据存储认证的重要原因，?NVIDIA 云合作伙_NCPQ提供支持。他们在构?AI 云、GPU x务或其他C代云产品Ӟ能够提供最快、最h展?/strong>的数据基设施。该认证Z WEKA 此前获得?NVIDIA HGX H100/H200 pȝ认证QƈZ Yotta、Ori Cloud、Sustainable Metal Cloud {众多领先新型云服务商的长期合作带来更多价倹{?/p>

Z么这臛_重要QAI 不仅更快Q而是本质上不?/h2>

AI 的发展正l历深刻变革。随着推理模型?AI 代理的普及，AI 工作负蝲变得更加复杂Q不仅需要高速通信、充_存及强大计算能力Q还要能够实时生成和处理量数据 TokensQ这要求数据基础设施必须能够?GPU 高性能协同工作?/p>

 

但传l的数据存储在以下几斚w存在严重短板Q?/p>

  • 性能鸿沟Q?/strong>传统存储无法满C AI 工作负蝲的对 I/O 的高要求Q计与数据基础设施之间的高延迟D GPU 利用率不?/li>
  • 扩展困境Q?/strong>许多服务商往往不得不过度配|存储来满性能目标Q从而大q推高成本?/li>
  • 多租h持不I传统存储~Z有效隔离机制Q迫使服务商为每个客户徏立低效的存储孤岛?/li>
  • q维成本和复杂性高Q?/strong>Z复制的传l容灾模型进一步抬高了理成本?/li>

WEKA + NVIDIA GB200 Qؓ AI 时代量n打?/h2>

WEKA 现已获得 NVIDIA GB200 部v高性能存储认证QNCP 可利?WEKA 全面提升 AI 云服务能力：

  • 极致的性能Q?/strong>WEKA 零调优架构能动态适配M工作负蝲Q实C毫秒Ugq和百万U?IOPS。单?8U 基础配置卛_满 GB200 Blackwell 可扩展集（1,152?GPUQ的极端 I/O 需求?/li>
  • 面向 AI 道优化?S3 对象存储Q?/strong>WEKA 通过优化?S3 对象存储接口Qؓ AI、机器学习和数据分析工作负蝲中的对象的讉K提供低延迟和高吞吐量支持?/li>
  • 最大化 GPU 利用率：存储瓉常常扼杀 AI 性能Q?WEKA 能帮助消除这一障碍Q数据性能提升 10 倍或更多。在实际部v中，客户?GPU 利用率从 30-40% 跃升臌q?90%?/li>
  • 真正的多U户支持Q?/strong>WEKA 的可l合集群利用容器技术实现逻辑与物理双重隔，从而提供安全、高性能?AI 云多U户服务Q实现性能零妥协?/li>
  • 大规模扩展能力：WEKA 在单一命名I间内可支持多达 32,000 ?NVIDIA GPU 集群Q助力英伟达云合作伙伴在全球范围内实C PB ?EB U的无缝扩展Q而无需担心架构受限?/li>
  • 无缝q移Q?/strong>无论是在数据中心、超大规模云q是C代云环境QWEKA l一的Y件架构均可实现工作负载的无缝q移?/li>

基准试与实际性能表现

WEKApod Nitro 讑֤?NVIDIA 云合作伙伴部|提供了卓越的性能密度和能效：

WEKApod Nitro 讑֤
WEKApod Nitro 讑֤
  • 吞吐性能Q?/strong>WEKApod 单节点实?70GB/s d速度Q最低配|下可达 560GB/sQ和 40GB/s 写入速度Q最低配|下可达 320GB/sQ，保 Blackwell GPU 持箋获得高速数据，最大化利用率?/li>
  • 延迟优化Q?/strong>亚毫U延迟Q确?AI 训练和推理工作负载gq最化Q助力实时推?AI 模型高效q行?/li>
  • 扩展性实践：借助 WEKApodQNCP 已实C PB U到 EB U的数据规模扩展Q支持数千个q发工作负蝲而不牺牲性能?/li>
  • GPU 利用率提升：WEKA 的可l合集群利用容器技术实现逻辑与物理双重隔，从而提供安全、高性能?AI 云多U户服务Q实现性能零妥协?/li>
  • 能效优化Q?/strong>WEKApod 优化的数据处理显著降低了每个 AI 工作负蝲的能耗，?AI 云服务商降低了整体运营成本?/li>
  • NVIDIA 认证Q?/strong>WEKA 荣获 NVIDIA pȝ存储解决Ҏ认证Q确保ؓ AI 和数据分析工作负载提供高性能、可扩展且可靠的存储解决Ҏ?/li>

NVIDIA MGX 部v的推荐存储配|?/h2>

合理的存储配|对保 AI 训练和推理的最x能臛_重要。存储性能目标因模型类型、数据集大小和工作负载特征而异。ؓ支持 NVIDIA MGX pȝ上的高性能训练和推理，WEKA Data Platform 提供了一?NVIDIA Blackwell 认证的、可扩展且高吞吐量的存储ҎQ完契合现?AI 工作负蝲的需求?/p>

 

对于大规?AI 训练来说Q检查点的读写性能ؓ关键Q这是一个同步Q务，若优化不当，可能会导致训l停滞。大型语a模型QLLMQ在查点阶段需极高写入吞吐量，光求也会随着模型规模增长而增加。例如，一?300 亿参数模型可能需?206 GB/s 的d入速率Q而一?1 万亿参数模型则可能需要近 389 GB/s 的写入速率?/p>

 

下表展示?WEKApod Nitro 讑֤在满?NVIDIA GB200 NVL72 机架Q最低存储容?10,924TBQ增强型指导/性能要求下的存储密度与认证性能Q?/p>

通过使用 WEKApodQ云服务商可以彻底消除存储瓶颈，保 Blackwell GPU 始终以最佳状态运行，而无需q度配?/p>

未来已来Q构?AI 推理时代的基设施

AI 推理时代需要一U全新的数据基础设施Q它不仅要运行快速、高效，q能应对 Token l济的爆发式增长。现在，借助 WEKA ?NVIDIA GB200 NVL72 强强联手QAI 云服务商可以同时获得的性能、强大的扩展性和安全保障ѝ?/p>

 

如果您是正在构徏下一?AI 云服务的 NVIDIA 云合作伙_现在正是解锁 GPU 潜能的最x机，?WEKA 携手Q让您的 AI 云服务更加快速、稳定、简单！

WEKA 携手 NVIDIA BlackwellQ释?AI 推理无限潜能

联系瑞技

您正在ؓ您的 AI 云服务寻扄破性的性能、弹性、可扩展性和数据灉|性吗Q立卌pȝ技?/p>

WEKA 携手 NVIDIA BlackwellQ释?AI 推理无限潜能最先出现在瑞技U技?/p> ]]> Supermicro 联手 NVIDIA Spectrum?X 推出针对 AI 优化的系l?/title> <link>//51269984.cn/bytebridge-blog/networking-hardware-devices-for-nvidia-spectrum-x.html?utm_source=rss&utm_medium=rss&utm_campaign=networking-hardware-devices-for-nvidia-spectrum-x</link> <dc:creator><![CDATA[BBT Partner Channel]]></dc:creator> <pubDate>Thu, 05 Dec 2024 02:22:36 +0000</pubDate> <category><![CDATA[blog]]></category> <category><![CDATA[AI]]></category> <category><![CDATA[NVIDIA]]></category> <category><![CDATA[Supermicro]]></category> <guid isPermaLink="false">//51269984.cn/?p=26453</guid> <description><![CDATA[<p>AI 正在推动Ҏ先进计算基础设施的需求，包括|络。网l带宽的不常常成ؓ驱动 AI 应用信息处理的瓶颈。计?[…]</p> <p><a href="//51269984.cn/bytebridge-blog/networking-hardware-devices-for-nvidia-spectrum-x.html">Supermicro 联手 NVIDIA Spectrum?X 推出针对 AI 优化的系l?/a>最先出现在<a href="//51269984.cn">瑞技U技</a>?/p> ]]></description> <content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="26453" class="elementor elementor-26453" data-elementor-post-type="post"> <section class="elementor-section elementor-top-section elementor-element elementor-element-53ffd14 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="53ffd14" data-element_type="section" data-settings="{"ekit_has_onepagescroll_dot":"yes"}"> <div id="ycaufmklw0" class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-36abd74" data-id="36abd74" data-element_type="column"> <div id="ycaufmklw0" class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-de37c7a elementor-widget elementor-widget-text-editor" data-id="de37c7a" data-element_type="widget" data-settings="{"ekit_we_effect_on":"none"}" data-widget_type="text-editor.default"> <div id="ycaufmklw0" class="elementor-widget-container"> <p>AI 正在推动Ҏ先进计算基础设施的需求，包括|络。网l带宽的不常常成ؓ驱动 AI 应用信息处理的瓶颈。计基设施的速度仅取决于其最q节，而这个环节往往是|络。通过与NVIDIA合作Q?a href="/supermicro" target="_blank" rel="noopener"><span style="text-decoration: underline;">Supermicro</span></a> 已经在其多个pȝ中验证了新的 NVIDIA Spectrum-X |络q_Q允许AI 工作负蝲的网l性能提高多达1.6倍，同时增加能效q低M成本。结?NVIDIA Spectrum-4以太|交换机?NVIDIA BlueField]-3 SuperNICQ客L在可以最大化 AI 云处理基设施的性能?/p><p><br />目前正在验证配备 NVIDIA Spectrum-X |络解决Ҏ的系l包?Supermicro 明星 GPU 服务器，包括4U?U?UQƈ支持以下pȝ中的 NVIDIA GPUQ?/p> </div> </div> <div class="elementor-element elementor-element-38ae6b7 elementor-widget elementor-widget-spacer" data-id="38ae6b7" data-element_type="widget" data-settings="{"ekit_we_effect_on":"none"}" data-widget_type="spacer.default"> <div id="ycaufmklw0" class="elementor-widget-container"> <div id="ycaufmklw0" class="elementor-spacer"> <div id="ycaufmklw0" class="elementor-spacer-inner"></div> </div> </div> </div> <div class="elementor-element elementor-element-a817dbb elementor-widget elementor-widget-text-editor" data-id="a817dbb" data-element_type="widget" data-settings="{"ekit_we_effect_on":"none"}" data-widget_type="text-editor.default"> <div id="ycaufmklw0" class="elementor-widget-container"> <ul><li><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":180,"335559740":360}"> </span><span data-contrast="none">搭蝲 </span><span data-contrast="none">NVIDIA HGX H100 8-GPU</span><span data-contrast="none"> pȝ?</span><span data-contrast="none">Supermicro 8U GPU</span><span data-contrast="none"> 服务器：</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li></ul><ol><li><span data-contrast="none">SYS-821GE-TNHR (8U)</span><span data-contrast="none"> ?</span><span data-contrast="none">Intel CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li><li><span data-contrast="none">AS-8125GS-TNHR (8U) ?AMD CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li><li><span data-contrast="none">SYS-421GU-TNXR (4U)</span><span data-contrast="none"> ?</span><span data-contrast="none">Intel CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li></ol><ul><li><span data-contrast="none">搭蝲 </span><span data-contrast="none">NVIDIA H100 PCIe GPU</span><span data-contrast="none"> ?</span><span data-contrast="none">NVIDIA L40S GPU</span><span data-contrast="none"> ?</span><span data-contrast="none">Supermicro</span><span data-contrast="none"> 通用 </span><span data-contrast="none">GPU</span><span data-contrast="none"> 服务器（4U?UQ：</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li></ul><ol><li><span data-contrast="none">SYS-521GE-TNRT (5U)</span><span data-contrast="none"> ?</span><span data-contrast="none">Intel CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li><li><span data-contrast="none">SYS-421GE-TNRT (4U)</span><span data-contrast="none"> ?</span><span data-contrast="none">Intel CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li><li><span data-contrast="none">SYS-421GE-TNRT3 (4U)</span><span data-contrast="none"> ?</span><span data-contrast="none">Intel CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li><li><span data-contrast="none">AS-4125GS-TNRT (4U)</span><span data-contrast="none"> ?</span><span data-contrast="none">AMD CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li><li><span data-contrast="none">AS-4125GS-TNRT2 (4U) ?AMD CPU</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245417":false,"201341983":0,"335551550":6,"335551620":6,"335557856":16777215,"335559738":0,"335559739":0,"335559740":360}"> </span></li></ol> </div> </div> <div class="elementor-element elementor-element-66b93b2 elementor-widget elementor-widget-spacer" data-id="66b93b2" data-element_type="widget" data-settings="{"ekit_we_effect_on":"none"}" data-widget_type="spacer.default"> <div id="ycaufmklw0" class="elementor-widget-container"> <div id="ycaufmklw0" class="elementor-spacer"> <div id="ycaufmklw0" class="elementor-spacer-inner"></div> </div> </div> </div> <div class="elementor-element elementor-element-96e61ec elementor-widget elementor-widget-image" data-id="96e61ec" data-element_type="widget" data-settings="{"ekit_we_effect_on":"none"}" data-widget_type="image.default"> <div id="ycaufmklw0" class="elementor-widget-container"> <img decoding="async" width="980" height="276" src="//51269984.cn/wp-content/uploads/2024/12/data-centers-ai-optimized-nvidia-spectrum-x-2.webp" class="attachment-large size-large wp-image-26456" alt="配备 NVIDIA Spectrum-X |络解决Ҏ?Supermicro 明星 GPU 服务? srcset="//51269984.cn/wp-content/uploads/2024/12/data-centers-ai-optimized-nvidia-spectrum-x-2.webp 992w, //51269984.cn/wp-content/uploads/2024/12/data-centers-ai-optimized-nvidia-spectrum-x-2-300x84.webp 300w, //51269984.cn/wp-content/uploads/2024/12/data-centers-ai-optimized-nvidia-spectrum-x-2-768x216.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div> </div> <div class="elementor-element elementor-element-b0c3adb elementor-widget elementor-widget-spacer" data-id="b0c3adb" data-element_type="widget" data-settings="{"ekit_we_effect_on":"none"}" data-widget_type="spacer.default"> <div id="ycaufmklw0" class="elementor-widget-container"> <div id="ycaufmklw0" class="elementor-spacer"> <div id="ycaufmklw0" class="elementor-spacer-inner"></div> </div> </div> </div> <div class="elementor-element elementor-element-e4a9aec elementor-widget elementor-widget-text-editor" data-id="e4a9aec" data-element_type="widget" data-settings="{"ekit_we_effect_on":"none"}" data-widget_type="text-editor.default"> <div id="ycaufmklw0" class="elementor-widget-container"> <p>q些?NVIDIA Spectrum-X 驱动?Supermicro pȝ减少了基于大型变换器的生成式 AI 模型的训l和推理旉Q这对于?AI 中快速获得数据至关重要?/p><p><br />除了?AI 极大提高1.6倍的|络性能外，配备 NVIDIA Spectrum-X |络?Supermicro pȝq从多租L境中隔离工作负蝲性能中受益。允许工作负载独立于Ud数据Q不受可能阻性能的“嘈杂邻居”干扎ͼq对于高性能应用臛_重要。此外，隔离|络工作负蝲q提供了额外的安全优势，可以防止一个工作负载中的恶意行干扰另一个独立的工作负蝲。NVIDIA Spectrum-X 通过为每个租户分配安全的虚拟U有云（VPCsQ来实现q种增强的工作负载隔R?/p><p><br />能效一直是q行高性能、高功率 AI pȝ时的主要x炏V通过在相同的|络功耗范围内提供更好的网l性能QNVIDIA Spectrum-X 可以为更节能?AI 环境做出贡献Q从而降低整体功耗和M拥有成本。对于控制能源成本的用户Q配?Spectrum-X ?Supermicro pȝ是一个不错的选择?/p><p><br />NVIDIA Spectrum-X 完全Z标准设计Qƈ且与Z以太|的堆栈可以实现完全互操作。此外，配备 NVIDIA Spectrum-X ?Supermicro 服务器可以帮助客户克服部|?AI 的最大挑战——可扩展性。凭借在单个交换Z支持256?00G端口或在两层?脊拓扑中支持16K端口的能力，q些pȝ非常适合需要不断扩展基设施的组l?/p><p><br />随着 Supermicro 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