--- 格式版本: 2 标题: "HPE Targets GPU Utilization With New AI Networking Portfolio" 原文链接: "https://www.datacenterknowledge.com/networking/hpe-targets-gpu-utilization-with-new-ai-networking-portfolio" 发布日期: "2026-08-18" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "llm:scrape:strict_markdown_body" 发布时间证据: "AUG 18, 2026" 发布时间校准原因: "正文标题附近出现的最晚日期，与发现时间吻合，且为临近的最新日期，符合新闻发布时间惯例。" 发布时间校准置信度: "1" 发布时间候选数量: 7 发布时间严格候选数量: 7 发布时间原页读取状态: "" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-18T22:17:58+08:00" 发布时间仲裁状态: "confirmed" 发布时间仲裁尝试次数: 1 发布时间仲裁耗时毫秒: 7279 发现时间: "2026-08-18T22:17:31+08:00" 入库时间: "2026-08-18T14:18:17.654Z" 来源平台: "Data Center Knowledge 搜索" 搜索渠道: "source_template" 搜索词: "https://www.datacenterknowledge.com/search?q=Compute%20tray" 匹配关键词: - "Compute tray" - "GPU" - "AI" - "Switch tray" - "Scale-up" - "Liquid Cooling" 相关厂家: - "NVIDIA" - "AMD" - "Google" 相关专家: [] 内容类型: "网页" 抓取工具: "CDP Render" 清洗工具: "CDP Text + Defuddle/Readability 正文提取" 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: "正文直接讨论HPE AI网络组合，涉及rack-scale AI平台、GPU利用率、QFX5252 switch tray等，与超节点/AI Rack高度相关，来源专业媒体，内容新且有技术细节和商业合作信息。" AI质检模型: "tx-deepseek-v4-flash" AI质检时间: "2026-08-18T22:19:03+08:00" AI主题相关性: 20 AI来源权威性: 14 AI新颖性: 18 AI技术细节: 16 AI商业部署信号: 13 AI完整性: 9 采集批次: "2026年8月18日20点38分13秒" 采集批次ID: "20260818-203813-342" 去重键: "https://www.datacenterknowledge.com/networking/hpe-targets-gpu-utilization-with-new-ai-networking-portfolio" --- An Informa TechTarget Publication NEWSLETTER SIGN-UP NETWORKING DATA CENTER HARDWARE AI DATA CENTERS DATA CENTER CHIPS NEWS HPE Targets GPU Utilization With New AI Networking Portfolio New Juniper-based switches, automation capabilities, and security integrations extend HPE’s networking strategy across AI training, inference, and enterprise environments. Shane Snider,Senior News Writer,Data Center Knowledge June 17, 2026 4 Min Read Rami Rahim, executive vice president, president and general manager of networking at HPE.PHOTO BY SHANE SNIDER Hewlett Packard Enterprise (HPE) has expanded its AI networking portfolio with new Juniper-based switching products, deeper integration of Juniper technology into its AI Data Center Solution, and additional automation capabilities designed to improve utilization of increasingly expensive AI infrastructure. The announcements, unveiled at HPE Discover 2026, extend the company’s effort to integrate Juniper Networks into a unified AI infrastructure stack spanning data center networking, operations, security, and enterprise connectivity. Networking Moves Deeper Into the AI Stack At the core of the announcement is the expansion of the HPE AI Data Center Solution to include HPE Juniper Networking QFX switches managed through HPE Networking Data Center Director. The move pulls Juniper’s data center networking portfolio deeper into HPE’s AI infrastructure stack, which combines compute, storage, networking, software, and services. Related:HPE Puts Networking at the Center of AI at Discover 2026 Speaking with reporters at HPE Discover, Rami Rahim, executive vice president, president and general manager of networking at HPE, said networking has become a determining factor in the economics of AI infrastructure. “People have come to the realization that if your network has congestion, reliability problems, what will happen is those GPUs that you spent hundreds of millions or billions on could be used at 75% utilization, 50% utilization, 25% utilization,” Rahim said. “Networking has truly become a force multiplier for massive AI data center investments.” J.J. Kardwell, CEO of cloud provider Vultr, which recently signed a partnership with HPE, said networking has become increasingly important as AI clusters scale. “The impact of the network is, frankly, larger than it’s ever been because of the architecture needs of those systems,” Kardwell said during a media briefing at HPE Discover. Kardwell said the challenge extends beyond individual GPU servers to the networks connecting racks, clusters, and data centers. “The network between those cabinets and across those clusters becomes the key factor in accomplishing these massive training and inferencing workloads,” he said. Networking and AI Infrastructure Economics Sameh Boujelbene, vice president at Dell’Oro Group, said networking is becoming a critical factor in AI infrastructure economics, as operators focus on turning expensive compute investments into production systems. “AI infrastructure is no longer just a GPU race,” Boujelbene told Data Center Knowledge. “It is a systems race, and networking is becoming one of the key economic levers that determines who can turn raw compute into usable intelligence at scale and profitably.” Related:HPE, Vultr Go All In on AI Inference Data Center Growth The new products span AI training clusters, inference environments, data center interconnects, and edge deployments, reflecting growing demand for networking architectures that support the full AI lifecycle. HPE also introduced two new AI networking products. The HPE Juniper Networking QFX5140 switch targets inference clusters and edge AI deployments, while a new QFX5252 switch tray is designed for AMD's Helios rack-scale AI platform. HPE said the products are intended to reduce networking delays that leave GPUs waiting for data rather than processing workloads. Rahim said the acquisition has also enabled HPE to address both scale-out and scale-up AI networking architectures. While Juniper had already established positions in routing and scale-out networking, he said tighter integration with HPE compute systems has accelerated development of scale-up technologies such as those being deployed in AMD Helios. Mist and Marvis Expand Across the Portfolio HPE is also extending Juniper’s Mist platform deeper into the Aruba installed base. The company said HPE Networking CX switches will now be supported in the Mist platform, providing customers with AI-driven visibility, automated troubleshooting, service-level insights, and Marvis AI actions. HPE is also bringing Marvis self-driving capabilities into Aruba Central, including automated remediation functions such as wired port troubleshooting. Related:HPE Interview: Why Data Center Efficiency Is Now Core to IT Decisions New Mist capabilities include predictive analytics for optics and system failures, along with an AI reasoning engine that draws on operational telemetry, support cases, and network data to accelerate root-cause analysis and remediation. Rahim said more than 80% of network incidents in self-driving deployments are now either automatically remediated or accompanied by immediate root-cause information for operators. Kardwell said the growing complexity of AI infrastructure is increasing the value of automated operations. “The cost of failure and downtime is unacceptable. The ability of self-healing is so important,” he said. Security and Operations Converge HPE also expanded integrations between networking and infrastructure management platforms. HPE Mist Networking Data Center Assurance is now integrated with HPE Compute Ops Management and GreenLake, providing operators with a more unified view of networking and compute infrastructure. On the security side, HPE unveiled a unified secure access service edge (SASE) platform that combines SD-WAN and security service edge capabilities through a single management console. Built on HPE Networking EdgeConnect, the platform incorporates zero-trust access controls and AI-assisted operations intended to simplify security and network management. One surprise since the deal closed, Rahim said, has been the speed of integration. “Things that I thought would be really difficult – getting teams together, making them feel like they’re on the same team, making the tough decisions about roadmaps and so forth – have all gone easier than I expected,” he said. About the Author Shane Snider Senior News Writer, Data Center Knowledge Shane Snider is Senior News Writer at Data Center Knowledge, covering AI infrastructure, hyperscale data centers, cloud platforms, and the power and energy systems driving modern compute expansion. His reporting focuses on the operational, economic, and environmental forces reshaping digital infrastructure, including AI factories, utility constraints, liquid cooling, renewable energy procurement, and next-generation data center architectures. He has won recent Azbee awards for news series and government reporting. Based in Raleigh, North Carolina, Snider covers how hyperscalers, utilities, chipmakers, and infrastructure providers are responding to the rapid rise of AI workloads and global compute demand. You can reach Shane at shane.snider@informatechtarget.com or on LinkedIn. Want more Data Center Knowledge stories in your Google search results? 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