--- 格式版本: 2 标题: "Google is a Leader in Gartner® Magic Quadrant for AI Infra" 原文链接: "https://cloud.google.com/blog/topics/ai-infrastructure/google-is-a-leader-in-gartner-magic-quadrant-for-ai-infra" 发布日期: "2026-07-08" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:scrape:provider_published_at" 发布时间证据: "provider publishedAt: 2026-07-08" 发布时间校准原因: "规则确认唯一严格发布时间，来源 scrape:provider_published_at" 发布时间校准置信度: "high" 发布时间候选数量: 1 发布时间严格候选数量: 1 发布时间原页读取状态: "原页面反爬/安全验证页" 发布时间未找到原因: "" 发布时间校准时间: "2026-07-29T18:19:32+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-07-29T18:15:25+08:00" 入库时间: "2026-07-29T10:19:32.945Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://cloud.google.com/blog/" 匹配关键词: - "A5X" - "Vera Rubin" - "SRAM" - "deployment" - "performance" - "latency" - "bandwidth" - "throughput" 相关厂家: - "Google" - "NVIDIA" 相关专家: [] 内容类型: "网页" 抓取工具: "Free Fetch + Defuddle" 清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 60 AI分档: "召回候选" AI质检状态: "不通过" AI打分理由: "该文为Google Cloud官方宣传稿，聚焦Gartner市场认可和云AI基础设施整体能力，涉及TPU、Virgo网络等，但未深入讨论超节点/AI Rack机柜级架构、供电、散热、互连等具体硬件细节，与项目核心主题关联较弱。" AI质检模型: "deepseek-v4-flash" AI质检时间: "2026-07-30T01:46:26+08:00" AI主题相关性: 5 AI来源权威性: 15 AI新颖性: 10 AI技术细节: 10 AI商业部署信号: 10 AI完整性: 10 采集批次: "2026年7月29日18点11分54秒" 采集批次ID: "20260729-181154-692" 去重键: "https://cloud.google.com/blog/topics/ai-infrastructure/google-is-a-leader-in-gartner-magic-quadrant-for-ai-infra" --- AI infrastructure ## Google Cloud named Leader in the 2026 Gartner® Magic Quadrant™ for AI Infrastructure ##### Mark Lohmeyer VP and GM, AI and Computing Infrastructure ##### Try Gemini Enterprise Business Edition today The front door to AI in the workplace [Try now](https://business.gemini.google/?utm_source=cloud.google.com/blog&utm_medium=et&utm_campaign=FY26-Q2-GLOBAL-GLO27877-physicalevent-er-next26-mc-105752) In the agentic era, AI is evolving from answering questions to reasoning and taking action. Companies who want to lead in this next phase of AI need computing infrastructure that’s designed and optimized for these new requirements, helping them innovate faster, deliver compelling user and customer experiences, and optimize for cost and energy efficiency — all at massive scale. **Today, we are pleased to announce that Google has been named a Leader in the inaugural Gartner Ⓡ Magic Quadrant™ for AI Infrastructure, positioned highest for ‘Ability to Execute’ and furthest for ‘Completeness of Vision’.** We believetheir findings validate our dedication to solving these challenges internally and for our customers. ![https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_05vW3xz.max-1200x1200.png](https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_05vW3xz.max-1200x1200.png) Read the full report: [2026 Gartner Magic Quadrant™ for AI Infrastructure](https://cloud.google.com/resources/content/2026-gartner-mq-ai-infrastructure) ### Building on the infrastructure foundation powering Gemini Today’s model and serving architectures require a fundamental rethinking of how silicon and software interact. We realized early on that the platform we envisioned couldn’t be bought off the shelf — we had to invent it. For over a decade, our infrastructure engineers and Google DeepMind researchers have worked shoulder to shoulder to co-design the entire stack for Gemini, YouTube, and Search. We make those innovations, together with popular third party and open source software, available to our customers through Google Cloud. Today our integrated stack serves 9 out of 10 frontier AI labs; capital markets firms like Citadel Securities; and enterprises like Mercedes Benz. At the hardware layer, Gartner recognized our commitment to custom silicon as a core strength. Earlier this year we shared two new advancements in custom silicon, our 8th generation TPUs, engineered to solve enterprise scaling and memory bottlenecks at a systems level: - **TPU 8t, the training powerhouse:** Purpose-built to optimize training timelines, TPU 8t packs 9,600 chips into a single superpod, delivering the high-density compute required for frontier models with nearly 3x the compute performance per pod over the previous generation. - **TPU 8i, the inference engine:** Engineered to handle the collaborative, iterative work of specialized agents, TPU 8i breaks the memory wall for real-time agentic workflows, with 288 GB of high-bandwidth memory and 384 MB of on-chip SRAM — 3x more than the previous generation — keeping a model's active working set entirely on-chip. While our TPU platforms push the boundaries of what is possible, we know that one size doesn't fit all. Different customers have different workloads, different requirements, and different use cases. So, we also partner deeply with NVIDIA to deliver the latest accelerated computing platforms as highly performant, reliable and scalable services in Google Cloud. We will be among the first to deliver A5X instances based on the next-generation Vera Rubin platform when it becomes available later this year, enabling customer choice. We also work closely with NVIDIA to integrate GPUs into many Google Cloud software services to give our customers easier access to accelerated computing. To enable even more flexibility, we continue to contribute to open-source projects across the orchestration, inference engines, and framework layers through llm-d and vLLM. We also recently announced TorchTPU, which gives PyTorch developers portability without complex code rewrites while maximizing the performance of their deployment. ### Get more performance per dollar on AI Hypercomputer As your infrastructure investment grows, you need to balance raw performance and cost to make AI applications economically viable. Taking a ‘buy now, integrate later’ approach to AI is becoming unsustainable. By combining pre-integrated hardware and open software frameworks that feature flexible consumption models, we deliver a unified system engineered for better performance per dollar across training, reinforcement learning, and inference. Gartner recognized our integrated AI Hypercomputer as a core strength. This AI-optimized infrastructure is engineered to drastically improve your performance per dollar: - A massive compute cluster is only as effective as the storage system feeding it data. Google Cloud Managed Lustre, powered by our new C4NX instances and Hyperdisk Exapools, now delivers 10 TB/s of bandwidth — up to 20x faster than other hyperscalers — while Rapid Buckets transforms object storage with up to 20 million operations per second, helping ensuring large-scale training checkpoints and recoveries happen near-instantly. - Our Virgo Network provides a high-bandwidth scale-out fabric capable of connecting **more than one million TPUs** across multiple data center sites into a training cluster, or **up to 960,000 GPUs** across multiple sites without performance degradation — transforming globally distributed infrastructure into a unified supercomputer. - GKE Inference Gateway enables scaling models in production with near-zero latency by combining LLM-aware routing, caching, and the disaggregated serving capabilities of llm-d, increasing throughput by up to 40% while reducing serving costs up to 30%. ### Run AI on a fluid infrastructure at virtually any scale In the agentic era, infrastructure cannot be a rigid, static constraint. It must be an intelligent resource that adapts to the shifting priorities of your business, scaling up with demand and down to zero when agents are idle, with consistent, reliable performance. On AI Hypercomputer, you can: - **Train smarter and faster,** using Cluster Director and Google Kubernetes Engine to scale up to 130,000 nodes. At the same time, squeeze up to 97% productivity (Goodput) out of every accelerator using TPU 8t together with software co-designed with Google DeepMind and integrated open-source frameworks — from JAX to Pathways and Pallas. - **Enable secure, low latency agent execution with GKE Agent Sandbox.** Because agents need to scale, GKE Agent Sandbox can sense agent bursts and respond rapidly — provisioning up to 300 sandboxes per second per cluster, then instantly scale back when agents sit idle, optimizing compute costs. - **Run distributed enterprise and AI workloads consistently across multicloud, edge, and on premises environments** with Cross-Cloud Network and Cloud WAN. This approach delivers low-latency, policy-driven connectivity across Google’s private global backbone spanning over 10+ million kilometers of fiber and over 200 countries and territories, with up to 40% higher performance than public internet routing. ### Take the next steps on your journey with AI Hypercomputer From frontier models, to billion user applications, [AI Hypercomputer](https://cloud.google.com/ai-infrastructure) gives you the purpose-built hardware, open software, and flexible consumption models you need to improve AI performance, cost, and developer productivity. We are honored to see decades of experience building scalable, affordable and reliable AI systems rewarded with a leadership position in Gartner’s research. You can download a complimentary copy of the [2026 Gartner Magic Quadrant™ for AI Infrastructure](https://cloud.google.com/resources/content/2026-gartner-mq-ai-infrastructure) on our website.