--- 格式版本: 2 标题: "NVIDIA Unveils Digital Blueprint for Building Next-Gen Data Centers | NVIDIA Blog" 原文链接: "https://blogs.nvidia.com/blog/omniverse-next-gen-data-center/" 发布日期: "2026-06-30" 发布时间校准状态: "found" 发布时间来源: "llm:strict_original_body" 发布时间证据: "time class=related-news-date nvidia-article-date datetime=2026-06-30T08:00:57-07:00: Jun 30, 2026" 发布时间校准原因: "该日期位于标题下方，且带有 nvidia-article-date 类名，符合文章发布时间的特征。" 发布时间校准置信度: "1" 发布时间候选数量: 36 发布时间严格候选数量: 12 发布时间原页读取状态: "原页面来自已抓取 HTML" 发布时间未找到原因: "候选日期无效或 LLM 未确认" 发布时间校准时间: "2026-07-20T09:53:51+08:00" 发现时间: "2026-07-20T09:23:48+08:00" 入库时间: "2026-07-20T02:13:45.793Z" 来源平台: "NVIDIA Blog 搜索" 搜索渠道: "source_template" 搜索词: "https://blogs.nvidia.com/?s=Switch%20tray" 匹配关键词: - "Switch tray" - "NVL72" - "Nvlink" 相关厂家: - "NVIDIA" 相关专家: [] 内容类型: "网页" 抓取工具: "AgentKey Scrape" 清洗工具: "AgentKey Markdown + LLM 正文裁剪" 原始附件: [] AI优质: "否" AI打分: 68 AI分档: "召回候选" AI质检状态: "不通过" AI打分理由: "NVIDIA官方发布，涉及GB200 NVL72液冷机柜及数字孪生设计流程，主题相关且来源权威。但正文侧重Omniverse软件工具链与仿真工作流，对超节点硬件架构、供电、互连等核心技术细节展开不足，商业部署信号较弱，完整性一般。" AI质检模型: "qwen3.6-plus" AI质检时间: "2026-07-20T10:13:45+08:00" AI主题相关性: 16 AI来源权威性: 15 AI新颖性: 14 AI技术细节: 12 AI商业部署信号: 6 AI完整性: 5 图片摘要: - "✓ ./assets/img-83d2f103.jpg | diagram | 展示了基于 Omniverse 的数字孪生数据中心3D模型，呈现了多排机架与液冷管线的物理布局。" - "✗ ./assets/img-1f6575f1.png | diagram | 图片主题为 NVIDIA Vera CPU，与正文 GB200/Grace CPU 及数据中心数字孪生主题不符" - "✗ ./assets/img-3511966c.jpg | photo | NVIDIA 品牌标识实拍图，属于装饰性/品牌宣传配图" - "✗ ./assets/img-e47e99dc.png | other | 图片主题为推理软件栈与 Token 成本，与正文数据中心物理设计主题不符" 采集批次: "2026年7月20日9点23分34秒" 采集批次ID: "20260720-092334-062" 去重键: "https://blogs.nvidia.com/blog/omniverse-next-gen-data-center" --- Designing, simulating and bringing up modern data centers is incredibly complex, involving multiple considerations like performance, energy efficiency and scalability. It also requires bringing together a team of highly skilled engineers across compute and network design, computer-aided design (CAD) modeling, and mechanical, electrical and thermal design. NVIDIA builds the world’s most advanced AI supercomputers and at GTC unveiled its latest — a large cluster based on the NVIDIA GB200 NVL72 liquid-cooled system. It consists of two racks, each containing 18 [NVIDIA Grace CPUs](https://www.nvidia.com/en-us/data-center/grace-cpu/) and 36 [NVIDIA Blackwell GPUs](https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing), connected by fourth-generation [NVIDIA NVLink](https://www.nvidia.com/en-us/data-center/nvlink/) switches. On the show floor, NVIDIA demoed this fully operational data center as a [digital twin](https://www.nvidia.com/en-us/glossary/digital-twin/) in [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/), a platform for connecting and building generative AI-enabled 3D pipelines, tools, applications and services. To bring up new data centers as fast as possible, NVIDIA first built its digital twin with software tools connected by Omniverse. Engineers unified and visualized multiple CAD datasets in full physical accuracy and photorealism in [Universal Scene Description](https://www.nvidia.com/en-us/omniverse/usd/) (OpenUSD) using the [Cadence](https://www.cadence.com/en_US/home.html) Reality digital twin platform, powered by NVIDIA Omniverse APIs. Accelerating Data Center Design With Digital Twins - YouTube NVIDIA 2.23M subscribers ## Design, Simulate and Optimize With Enhanced Efficiency and Accuracy The new GB200 cluster is replacing an existing cluster in one of NVIDIA’s legacy data centers. To start the digital build-out, technology company [Kinetic Vision](https://kinetic-vision.com/) scanned the facility using the [NavVis VLX wearable lidar scanner](https://www.navvis.com/vlx) to produce highly accurate point cloud data and panorama photos. Then, [Prevu3D](https://www.prevu3d.com/solutions/realitymesh-omniverse/) software was used to remove the existing clusters and convert the point cloud to a 3D mesh. This provided a physically accurate 3D model of the facility, in which the new digital data center could be simulated. Engineers combined and visualized multiple CAD datasets with enhanced precision and realism by using the Cadence Reality platform. The platform’s integration with Omniverse provided a powerful computing platform that enabled teams to develop OpenUSD-based 3D tools, workflows and applications. [Omniverse Cloud APIs](https://nvidianews.nvidia.com/news/omniverse-cloud-apis-industrial-digital-twin) also added interoperability with more tools, including PATCH MANAGER and NVIDIA Air. With PATCH MANAGER, the team designed the physical layout of their cluster and networking infrastructure, ensuring that cabling lengths were accurate and routing was properly configured. The team used Cadence’s Reality Digital Twin solvers, accelerated by [NVIDIA Modulus](https://developer.nvidia.com/modulus) APIs and NVIDIA Grace Hopper, to simulate airflows, as well as the performance of the new liquid-cooling systems from partners like Vertiv and Schneider Electric. The integrated cooling systems in the GB200 trays were simulated and optimized using solutions from Ansys, which brought simulation data into the digital twin. The demo showed how digital twins can allow users to fully test, optimize and validate data center designs before ever producing a physical system. By visualizing the performance of the data center in the digital twin, teams can better optimize their designs and plan for what-if scenarios. Users can also enhance data center and cluster designs by balancing disparate sets of boundary conditions, such as cabling lengths, power, cooling and space, in an integrated manner — enabling engineers and design teams to bring clusters online much faster and with more efficiency and optimization than before. Learn more about [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/) and [NVIDIA Blackwell](https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing). ![Why Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency](./assets/img-83d2f103.jpg)