--- 格式版本: 2 标题: "NVIDIA and Microsoft Showcase Blackwell Preview, Omniverse Industrial AI and RTX AI PCs | NVIDIA Blog" 原文链接: "https://blogs.nvidia.com/blog/microsoft-ignite-blackwell-omniverse-rtx-ai/" 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: "该日期来自HTML metadata中的article-date字段，且位于标题附近，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: 40 发布时间严格候选数量: 13 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: "2026-07-20T12:49:14+08:00" 发现时间: "2026-07-20T09:26:26+08:00" 入库时间: "2026-07-20T04:52:09.651Z" 来源平台: "NVIDIA Blog 搜索" 搜索渠道: "source_template" 搜索词: "https://blogs.nvidia.com/?s=Microsoft" 匹配关键词: - "GPU" - "NVL72" 相关厂家: - "Microsoft" - "NVIDIA" - "OpenAI" 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，提及Azure ND GB200 V6 VM系列基于GB200 NVL72机架设计，涉及超节点/AI Rack概念，但正文主要聚焦Azure云服务、Omniverse、RTX AI PC等应用层，缺乏机柜级架构、供电…" AI质检模型: "qwen3.6-plus" AI质检时间: "2026-07-20T12:52:09+08:00" AI主题相关性: 14 AI来源权威性: 15 AI新颖性: 16 AI技术细节: 10 AI商业部署信号: 10 AI完整性: 3 图片摘要: - "★ ./assets/img-83d2f103.jpg | diagram | 展示 NVIDIA GB200 NVL72 机架系统设计，体现 Blackwell 平台的高密度互连与 Quantum InfiniBand 网络架构。" - "✗ ./assets/img-1f6575f1.png | diagram | 正文未提及 Vera CPU，主要讨论 Blackwell 平台。" - "✗ ./assets/img-3511966c.jpg | photo | 品牌宣传/装饰性图片，无具体技术信息。" - "✗ ./assets/img-e47e99dc.png | infographic | 正文未详细讨论推理软件栈与 Token 成本，且图片主题似为独立技术文章。" 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/microsoft-ignite-blackwell-omniverse-rtx-ai" --- *Editor’s note: As of June 6, 2025, NVIDIA Edify is no longer available as an NVIDIA NIM microservice preview. To explore available visual AI models, visit* [*build.nvidia.com*](https://build.nvidia.com/explore/visual-design)*.* NVIDIA and Microsoft today unveiled product integrations designed to advance full-stack NVIDIA AI development on Microsoft platforms and applications. At [Microsoft Ignite](https://ignite.microsoft.com/en-US/home), Microsoft announced the launch of the first cloud private preview of the [Azure ND GB200 V6 VM series](https://azure.microsoft.com/en-us/blog/scale-your-ai-transformation-with-a-powerful-secure-and-adaptive-cloud-infrastructure/), based on the NVIDIA Blackwell platform. The Azure ND GB200 v6 will be a new AI-optimized virtual machine (VM) series and combines the [NVIDIA GB200 NVL72](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) rack design with NVIDIA Quantum InfiniBand networking. In addition, Microsoft revealed that Azure Container Apps now supports NVIDIA GPUs, enabling simplified and scalable AI deployment. Plus, the NVIDIA AI platform on Azure includes new reference workflows for industrial AI and an [NVIDIA Omniverse Blueprint for creating immersive, AI-powered visuals](https://developer.nvidia.com/blog/building-a-generative-ai-openusd-app-for-brand-accurate-marketing-visuals/). At Ignite, NVIDIA also announced multimodal small language models (SLMs) for RTX AI PCs and workstations, enhancing digital human interactions and virtual assistants with greater realism. ## NVIDIA Blackwell Powers Next-Gen AI on Microsoft Azure Microsoft’s new Azure ND GB200 V6 VM series will harness the powerful performance of NVIDIA GB200 Grace Blackwell Superchips, coupled with advanced [NVIDIA Quantum InfiniBand](https://www.nvidia.com/en-us/networking/quantum2/) networking. This offering is optimized for large-scale deep learning workloads to accelerate breakthroughs in natural language processing, computer vision and more. The Blackwell-based VM series complements previously announced [Azure AI clusters with ND H200 V5 VMs](https://azure.microsoft.com/en-us/blog/microsoft-launches-latest-azure-virtual-machines-optimized-for-ai-supercomputing-the-nd-h200-v5-series/), which provide increased high-bandwidth memory for improved AI inferencing. The ND H200 V5 VMs are already being used by OpenAI to enhance ChatGPT. ## Azure Container Apps Enables Serverless AI Inference With NVIDIA Accelerated Computing Serverless computing provides AI application developers increased agility to rapidly deploy, scale and iterate on applications without worrying about underlying infrastructure. This enables them to focus on optimizing models and improving functionality while minimizing operational overhead. The Azure Container Apps serverless containers platform simplifies deploying and managing microservices-based applications by abstracting away the underlying infrastructure. [Azure Container Apps](https://aka.ms/Ignite24/blog/serverlessGPU) now supports NVIDIA-accelerated workloads with serverless GPUs, allowing developers to use the power of accelerated computing for real-time AI inference applications in a flexible, consumption-based, serverless environment. This capability simplifies AI deployments at scale while improving resource efficiency and application performance without the burden of infrastructure management. Serverless GPUs allow development teams to focus more on innovation and less on infrastructure management. With per-second billing and scale-to-zero capabilities, customers pay only for the compute they use, helping ensure resource utilization is both economical and efficient. NVIDIA is also working with Microsoft to bring [NVIDIA NIM](https://www.nvidia.com/en-us/ai/) microservices to serverless NVIDIA GPUs in Azure to optimize AI model performance. ## NVIDIA Unveils Omniverse Reference Workflows for Advanced 3D Applications NVIDIA announced reference workflows that help developers to build 3D simulation and [digital twin](https://www.nvidia.com/en-us/glossary/digital-twin/) applications on [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/) and [Universal Scene Description (OpenUSD)](https://www.nvidia.com/en-us/omniverse/usd/) — accelerating industrial AI and advancing AI-driven creativity. A [reference workflow for 3D remote monitoring](https://developer.nvidia.com/blog/connect-real-time-iot-data-to-digital-twins-for-3d-remote-monitoring/) of industrial operations is coming soon to enable developers to connect physically accurate 3D models of industrial systems to real-time data from Azure IoT Operations and Power BI. These two Microsoft services integrate with applications built on NVIDIA Omniverse and OpenUSD to provide solutions for industrial IoT use cases. This helps remote operations teams accelerate decision-making and optimize processes in production facilities. The [Omniverse Blueprint](https://build.nvidia.com/nvidia/conditioning-for-precise-visual-generative-ai) for precise visual generative AI enables [developers to create applications](https://developer.nvidia.com/blog/building-a-generative-ai-openusd-app-for-brand-accurate-marketing-visuals/) that let nontechnical teams generate AI-enhanced visuals while preserving brand assets. The blueprint supports models like [SDXL](https://build.nvidia.com/stabilityai/stable-diffusion-xl) and [Shutterstock Generative 3D](https://build.nvidia.com/shutterstock/edify-3d) to streamline the creation of on-brand, AI-generated images. Leading creative groups, including [Accenture Song](https://www.accenture.com/us-en/about/accenture-song-index), [Collective](https://www.collectiveworld.com/), [GRIP](https://contact.indg.com/scaling-ad-production), [Monks](https://www.monks.com/) and [WPP](https://www.nvidia.com/en-us/industries/media-and-entertainment/wpp/), have adopted this NVIDIA Omniverse Blueprint to personalize and customize imagery across markets. ## Accelerating Gen AI for Windows With RTX AI PCs [NVIDIA’s collaboration with Microsoft](https://www.nvidia.com/en-us/data-center/gpu-cloud-computing/microsoft-azure/) extends to bringing AI capabilities to personal computing devices. At Ignite, NVIDIA announced its new multimodal SLM, [NVIDIA Nemovision-4B Instruct](https://blogs.nvidia.com/blog/ai-decoded-microsoft-ignite-rtx), for understanding visual imagery in the real world and on screen. It’s coming soon to RTX AI PCs and workstations and will pave the way for more sophisticated and lifelike digital human interactions. Plus, updates to [NVIDIA TensorRT Model Optimizer (ModelOpt)](https://github.com/NVIDIA/TensorRT-Model-Optimizer) offer Windows developers a path to optimize a model for ONNX Runtime deployment. TensorRT ModelOpt enables developers to create AI models for PCs that are faster and more accurate when accelerated by RTX GPUs. This enables large models to fit within the constraints of PC environments, while making it easy for developers to deploy across the PC ecosystem with ONNX runtimes. [RTX AI-enabled PCs and workstations](https://blogs.nvidia.com/blog/ai-decoded-microsoft-ignite-rtx) offer enhanced productivity tools, creative applications and immersive experiences powered by local AI processing. ## Full-Stack Collaboration for AI Development NVIDIA’s extensive ecosystem of partners and developers brings a wealth of AI and high-performance computing options to the Azure platform. SoftServe, a global IT consulting and digital services provider, today announced the availability of [SoftServe Gen AI Industrial Assistant](https://www.softserveinc.com/en-us/our-partners/nvidia/gen-ai-industrial-assistant), based on the [NVIDIA AI Blueprint](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag) for multimodal PDF data extraction, on the [Azure marketplace](https://azuremarketplace.microsoft.com/en-us/marketplace/consulting-services/softserveinc1605804530752.softserve_genai_industrial_assistant_2_weeks_imple?tab=Overview&filters=country-unitedstates). The assistant addresses critical challenges in manufacturing by using AI to enhance equipment maintenance and improve worker productivity. At Ignite, AT&T will showcase how it’s using NVIDIA AI and Azure to enhance operational efficiency, boost employee productivity and drive business growth through [retrieval-augmented generation](https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/) and autonomous assistants and agents. Learn more about [NVIDIA and Microsoft’s collaboration and sessions at Ignite](https://www.nvidia.com/en-us/events/microsoft-ignite/). *See* [*notice*](https://www.nvidia.com/en-us/about-nvidia/legal-info/) *regarding software product information.* ![Why Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency](./assets/img-83d2f103.jpg)