--- 格式版本: 2 标题: "NVIDIA and Google Partnership Gains Momentum With the Latest Blackwell and Gemini Announcements | NVIDIA Blog" 原文链接: "https://blogs.nvidia.com/blog/nvidia-google-blackwell-gemini/" 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，且包含 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: "100" 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: "2026-07-20T10:30:39+08:00" 发现时间: "2026-07-20T09:24:12+08:00" 入库时间: "2026-07-20T02:33:20.194Z" 来源平台: "NVIDIA Blog 搜索" 搜索渠道: "source_template" 搜索词: "https://blogs.nvidia.com/?s=Liquid%20Cooling" 匹配关键词: - "Liquid Cooling" - "NVL72" 相关厂家: - "NVIDIA" - "Google" 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GB200 NVL72及Google液冷基础设施，但正文侧重软件生态与云服务部署，缺乏超节点硬件架构、供电散热互连等核心技术细节，商业信号偏软。" AI质检模型: "qwen3.6-plus" AI质检时间: "2026-07-20T10:33:20+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机架级架构，体现大规模GPU互连与液冷管路设计，支撑AI Hypercomputer。" - "✗ ./assets/img-1f6575f1.png | diagram | 图片内容为NVIDIA Vera CPU架构，正文摘要仅涉及Blackwell架构与Google Cloud合作，主题不相关。" - "✗ ./assets/img-3511966c.jpg | photo | NVIDIA总部大楼与Logo照片，属于品牌宣传与装饰性配图，无实质技术信息。" - "✓ ./assets/img-e47e99dc.png | infographic | 展示NVIDIA推理软件栈概念图，对应NeMo、TensorRT-LLM等软件在Google Cloud的集成。" 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/nvidia-google-blackwell-gemini" --- NVIDIA and Google share a long-standing relationship rooted in advancing AI innovation and empowering the global developer community. This partnership goes beyond infrastructure, encompassing deep engineering collaboration to optimize the computing stack. The latest innovations stemming from this partnership include significant contributions to community software efforts like JAX, OpenXLA, MaxText and llm-d. These foundational optimizations directly support serving of Google’s cutting-edge Gemini models and Gemma family of open models. Additionally, performance-optimized NVIDIA AI software like [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/products/nemo/get-started/), [NVIDIA TensorRT-LLM](https://docs.nvidia.com/tensorrt-llm/index.html), [NVIDIA Dynamo](https://www.nvidia.com/en-us/ai/dynamo/) and [NVIDIA NIM microservices](https://developer.nvidia.com/nim) are tightly integrated across Google Cloud, including Vertex AI, Google Kubernetes Engine (GKE) and Cloud Run, to accelerate performance and simplify AI deployments. ## NVIDIA Blackwell in Production on Google Cloud Google Cloud was the first cloud service provider to offer both [NVIDIA HGX B200](https://www.nvidia.com/en-us/data-center/hgx/) and [NVIDIA GB200 NVL72](https://www.nvidia.com/en-us/data-center/gb200-nvl72/) with its A4 and A4X virtual machines (VMs). These new VMs with Google Cloud’s AI Hypercomputer architecture are accessible through managed services like Vertex AI and GKE, enabling organizations to choose the right path to develop and deploy agentic AI applications at scale. Google Cloud’s A4 VMs, accelerated by NVIDIA HGX B200, are now generally available. Google Cloud’s [A4X VMs](https://cloud.google.com/blog/products/compute/new-a4x-vms-powered-by-nvidia-gb200-gpus) deliver over one exaflop of compute per rack and support seamless scaling to tens of thousands of GPUs, enabled by Google’s Jupiter network fabric and advanced networking with [NVIDIA ConnectX-7 NICs](https://www.nvidia.com/en-us/networking/ethernet-adapters/). Google’s third-generation liquid cooling infrastructure delivers sustained, efficient performance even for the largest AI workloads. ## Google Gemini Can Now Be Deployed On-Premises With NVIDIA Blackwell on Google Distributed Cloud Gemini’s advanced reasoning capabilities are already powering cloud-based agentic AI applications — however, some customers in public sector, healthcare and financial services with strict data residency, regulatory or security requirements have yet been unable to tap into the technology. With NVIDIA Blackwell platforms coming to Google Distributed Cloud — Google Cloud’s fully managed solution for on-premises, air-gapped environments and edge — organizations will now be able to [deploy Gemini models](https://blogs.nvidia.com/blog/google-cloud-next-agentic-ai-reasoning/) securely within their own data centers, unlocking agentic AI for these customers NVIDIA and Google Cloud: Google Distributed Cloud (GDC) brings Google’s models on-prem - YouTube Google Cloud 352K subscribers NVIDIA Blackwell’s unique combination of breakthrough performance and confidential computing capabilities makes this possible — ensuring that user prompts and fine-tuning data remain protected. This enables customers to innovate with Gemini while maintaining full control over their information, meeting the highest standards of privacy and compliance. Google Distributed Cloud expands the reach of Gemini, empowering more organizations than ever to tap into next-generation agentic AI. ## Optimizing AI Inference Performance for Google Gemini and Gemma Designed for the agentic era, the Gemini family of models represent Google’s most advanced and versatile AI models to date, excelling at complex reasoning, coding and multimodal understanding. NVIDIA and Google have worked on performance optimizations to ensure that Gemini-based inference workloads run efficiently on NVIDIA GPUs, particularly within Google Cloud’s Vertex AI platform. This enables Google to serve a significant amount of user queries for Gemini models on NVIDIA-accelerated infrastructure across Vertex AI and Google Distributed Cloud. In addition, the [Gemma](https://build.nvidia.com/search?q=Gemma) family of lightweight, open models have been optimized for inference using the NVIDIA TensorRT-LLM library and are expected to be offered as easy-to-deploy [NVIDIA NIM](https://www.nvidia.com/en-us/ai-data-science/products/nim-microservices/) microservices. These optimizations maximize performance and make advanced AI more accessible to developers to run their workloads on various deployment architectures across data centers to local NVIDIA RTX-powered PCs and workstations. ## Building a Strong Developer Community and Ecosystem NVIDIA and Google Cloud are also supporting the developer community by optimizing open-source frameworks like JAX for seamless scaling and breakthrough performance on Blackwell GPUs — enabling AI workloads to run efficiently across tens of thousands of nodes. The collaboration extends beyond technology, with the launch of a new joint Google Cloud and NVIDIA [developer community](https://developers.google.com/community/nvidia?utm_source=linkedin&utm_medium=unpaidsoc&utm_campaign=fy25q2-googlecloud-blog-ai-in_feed-no-brand-global&utm_content=-&utm_term=-&linkId=14552521) that brings experts and peers together to accelerate cross-skilling and innovation. By combining engineering excellence, open-source leadership and a vibrant developer ecosystem, the companies are making it easier than ever for developers to build, scale and deploy the next generation of AI applications. *See* [*notice*](https://www.nvidia.com/en-eu/about-nvidia/terms-of-service/) *regarding software product information*. ![Why Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency](./assets/img-83d2f103.jpg) ![How NVIDIA’s Inference Software Stack Powers the Lowest Token Cost](./assets/img-e47e99dc.png)