--- 格式版本: 2 标题: "Anthropic’s Models Now Run on NVIDIA GB300 in Azure | NVIDIA Blog" 原文链接: "https://blogs.nvidia.com/blog/anthropic-nvidia-gb300-blackwell-ultra-microsoft-azure/" 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，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: "100" 发布时间候选数量: 38 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: "2026-07-20T11:12:21+08:00" 发现时间: "2026-07-20T09:24:42+08:00" 入库时间: "2026-07-20T03:17:42.598Z" 来源平台: "NVIDIA Blog 搜索" 搜索渠道: "source_template" 搜索词: "https://blogs.nvidia.com/?s=NVL72" 匹配关键词: - "NVL72" 相关厂家: - "NVIDIA" - "Microsoft" 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: "是" AI打分: 90 AI分档: "高置信优质" AI质检状态: "通过" AI打分理由: "NVIDIA官方发布，明确讨论GB300 NVL72超节点在Azure的商用部署，涉及Anthropic Claude模型集成与Quantum-X800网络，具备高权威性与强商业落地信号，技术细节略少但整体优质。" AI质检模型: "qwen3.6-plus" AI质检时间: "2026-07-20T11:17:42+08:00" AI主题相关性: 18 AI来源权威性: 15 AI新颖性: 18 AI技术细节: 14 AI商业部署信号: 15 AI完整性: 10 图片摘要: - "★ ./assets/img-83d2f103.jpg | diagram | NVIDIA GB300 NVL72系统机架级架构渲染图，展示高密度计算节点与互连布局。" - "✗ ./assets/img-1f6575f1.png | diagram | 图片展示Vera CPU与GPU协作，正文主要讨论GB300 Blackwell Ultra，主题不符。" - "✗ ./assets/img-3511966c.jpg | photo | NVIDIA品牌Logo及建筑实拍，属装饰性/品牌宣传图。" - "✗ ./assets/img-e47e99dc.png | infographic | Alt text指向推理软件栈文章，图片为抽象概念图，与正文GB300硬件主题关联弱。" 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/anthropic-nvidia-gb300-blackwell-ultra-microsoft-azure" --- [Anthropic’s Claude models](https://claude.com/blog/claude-in-microsoft-foundry) in Microsoft Foundry — hosted on Microsoft Azure and running on NVIDIA GB300 Blackwell Ultra GPUs — are now generally available, giving Azure-native enterprises a powerful new way to build autonomous and domain-specific AI agents. As agentic AI continues to drive enterprise innovation and becomes more autonomous, organizations need access to computing power to build and deploy specialized agents to accelerate essential business tasks. And having great inference performance and efficiency reduces total cost of ownership and drives positive company results. With Claude in Foundry running on [NVIDIA GB300 NVL72](https://www.nvidia.com/en-us/data-center/gb300-nvl72/) systems with [NVIDIA Quantum-X800 InfiniBand](https://www.nvidia.com/en-us/networking/products/infiniband/quantum-x800/) networking, enterprises can now build and run more powerful agentic systems, including autonomous and specialized sub-agents that can work across business domains to perform advanced tasks. ## A Growing Partnership NVIDIA is working with Anthropic to extend developer capabilities by integrating NVIDIA tools into the Anthropic stack. That integration enables enterprises to give Claude agents domain-specific abilities. Through NVIDIA verified [agent skills](https://github.com/nvidia/skills), enabled by access to NVIDIA accelerated computing, enterprises can embed AI agents deeply into their business and use them as the operating system for the organization. Enterprises can run Claude agents on Azure by using the [NVIDIA Secure Agent Workspace Reference Design](https://developer.nvidia.com/blog/how-to-govern-autonomous-agents-in-enterprise-ai-factories). It provides a blueprint for running autonomous agents in a governed environment where identity, network access, credentials and runtime policy are controlled at the infrastructure level. Claude in Microsoft Foundry accelerated by NVIDIA GB300 GPUs on Azure builds on the strategic partnership [Microsoft, NVIDIA and Anthropic announced in November](https://blogs.microsoft.com/blog/2025/11/18/microsoft-nvidia-and-anthropic-announce-strategic-partnerships/) to expand enterprise access to Claude and offer Anthropic models on NVIDIA accelerated computing. *Get started by visiting* [*Claude in Microsoft Foundry*](https://ai.azure.com/catalog/publishers/anthropic) *and learn more in* [*Foundry documentation*](https://aka.ms/ClaudeGAdocumentation)*.* ![Why Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency](./assets/img-83d2f103.jpg)