--- 格式版本: 2 标题: "AI Storage Ecosystem for the Data Center | NVIDIA" 原文链接: "https://www.nvidia.com/en-us/data-center/ai-storage/" 发布日期: "2026-08-04" 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:original_meta_loose" 发布时间证据: "nv-pub-date: 2026-08-04T13:20:00.000Z" 发布时间校准原因: "该候选为页面meta中的发布时间（nv-pub-date），符合优先级最高的published_time类型，且未被排除。" 发布时间校准置信度: "1" 发布时间候选数量: 6 发布时间严格候选数量: 0 发布时间原页读取状态: "原页面已读取" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-06T13:55:16+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: 5316 发现时间: "2026-08-06T13:44:31+08:00" 入库时间: "2026-08-06T05:55:22.330Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://www.nvidia.com/en-us/about-nvidia/careers" 匹配关键词: - "GPU" - "performance" - "latency" - "throughput" 相关厂家: - "NVIDIA" 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: "否" AI打分: 37 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "该页面为NVIDIA AI存储方案介绍，聚焦存储加速，未涉及超节点/AI Rack/机柜级系统、核心部件(Compute tray/Switch tray/GPU/CPO/800V/液冷)或量产落地，与项目主题相关性极低。" AI质检模型: "deepseek-v4-flash" AI质检时间: "2026-08-06T19:50:34+08:00" AI主题相关性: 2 AI来源权威性: 15 AI新颖性: 5 AI技术细节: 5 AI商业部署信号: 2 AI完整性: 8 采集批次: "2026年8月6日13点44分30秒" 采集批次ID: "20260806-134430-321" 去重键: "https://www.nvidia.com/en-us/data-center/ai-storage" --- ![Storage Ecosystem for the Data Center](https://www.nvidia.com/content/dam/en-zz/nvidiaweb/data-center/ai-storage/ai-storage-bm-af-bottom-p.jpg,%20/content/dam/en-zz/nvidiaweb/data-center/ai-storage/ai-storage-bm-af-bottom-p@2x.jpg "Storage Ecosystem for the Data Center") AI Storage Ecosystem for the Data Center ## AI Storage Accelerated storage infrastructure for AI workloads, built with partners. Overview ## Foundation for the New Frontier of AI Storage NVIDIA is empowering partners to build the first AI-native storage solutions, transforming passive data into active fuel for [AI factories](https://www.nvidia.com/en-us/solutions/ai-factories/). While traditional solutions were designed for general-purpose computing, agentic AI requires a new class of storage. By integrating accelerated computing and [networking](https://www.nvidia.com/en-us/networking/) into the storage fabric and optimizing the software stack, we help partners deliver hyperscale-grade efficiency across inference and training. This foundation allows organizations to turn their massive datasets into immediately accessible intelligence. ### NVIDIA Vera CPU Scales Storage Processing for AI Factories Storage benchmarks show how the NVIDIA Vera CPU in the NVIDIA BlueField™-4 STX Storage Processor delivers up to 3.2x more throughput than x86 in a two-stage compression and encryption pipeline. This helps AI-native storage platforms process and protect more data while keeping pace with agentic AI workloads. ### NVIDIA Vera BlueField-4 STX Brings Agentic AI Storage Processing With In-Silicon Security Embed agent, memory, and storage protection in silicon with NVIDIA® Vera BlueField®-4 STX, enforcing real-time, policy-driven security at AI agent speed. Architectures ## Explore Our AI Storage Architectures ### NVIDIA STX - Modular reference architecture for AI storage workloads - Co-designed with leading storage partners - Built on NVIDIA accelerated compute, networking, and AI software ### NVIDIA CMX™ Context Memory Storage - AI-native storage tier extending GPU context memory for long-context inference - Enables high-speed sharing of key-value (KV) cache across rack-scale nodes - Improves throughput and power efficiency over traditional storage ### NVIDIA AI Data Platform - Customizable reference design that integrates NVIDIA accelerated computing into enterprise storage - Centralizes intelligent data handling and delivers AI-ready data - Reduces latency, enhances data security, and maximizes performance ### NVIDIA-Certified Storage - Enterprise storage from leading NVIDIA partners - Certified to deliver performance, security, and scale required for production AI workloads - Enables faster model training, lower latency inference, and better infrastructure utilization Ecosystem ## AI Storage Partner Ecosystem Resources ## Learn More About AI Storage [See All](https://resources.nvidia.com/l/en-us-ai-storage)