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As artificial intelligence continues to transform industries, enterprises are now preparing for the next leap forward — agentic AI. These are intelligent systems that can reason, plan, and act autonomously to solve complex problems. To make this shift a reality, NVIDIA has partnered with the world’s leading storage and server manufacturers to launch the NVIDIA AI Data Platform — a customizable infrastructure blueprint purpose-built for agentic AI.
This article breaks down how top players in the storage and server hardware space are aligning their designs with NVIDIA’s cutting-edge reference model. It covers the growing ecosystem of partners, new hardware developments, and how this innovation will unlock the hidden value in enterprise data. Let’s dive into the summary of this breakthrough.
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NVIDIA is collaborating with the world’s top storage and server manufacturers to roll out the AI Data Platform, a reference design engineered to fuel the rise of agentic AI — systems that autonomously perform multistep reasoning and planning tasks. This platform combines NVIDIA’s accelerated computing, networking, and software with custom hardware from major partners to create a high-performance, scalable AI infrastructure.
Top-tier storage vendors like DDN, Dell, HPE, Hitachi Vantara, IBM, NetApp, Nutanix, Pure Storage, VAST Data, and WEKA are now deploying solutions built around this platform. These offerings integrate technologies like NVIDIA RTX PRO 6000 Blackwell GPUs, BlueField DPUs, and Spectrum-X Ethernet networking, allowing AI agents to search, analyze, and retrieve insights from vast troves of unstructured data such as PDFs, videos, and documents — in real time.
Original Design Manufacturers (ODMs) such as AIC, ASUS, Foxconn, Quanta Cloud Technology, Supermicro, and Wistron are also actively creating specialized hardware for this ecosystem. These companies bring decades of expertise in compact, energy-efficient server design and are essential to making scalable agentic AI a reality.
Key features of this ecosystem include the ability to bring compute power closer to data (thanks to embedded GPUs and DPUs), enhanced document indexing and security, and support for real-time AI inference. The system architecture is designed to facilitate retrieval-augmented generation (RAG) using NVIDIA’s NeMo Retriever microservices and the AI-Q Blueprint — enabling rapid knowledge extraction and contextual understanding.
Storage leaders like IBM, NetApp, and VAST Data have introduced tailored solutions built on NVIDIA’s reference model. IBM’s Fusion platform enhances inferencing for AI agents by offering content-aware storage. NetApp’s AIPod delivers scalable storage integration with NVIDIA microservices, while VAST Data’s unified platform supports intelligent multi-agent systems with continuous learning and secure data handling.
Meanwhile, ODMs are contributing cutting-edge designs for GPU servers and all-flash storage arrays that are optimized for AI Data Platform workloads. Innovations include ASUS’s software-defined storage, Supermicro’s Grace CPU-powered flash arrays, and Foxconn’s NVIDIA-accelerated servers.
Together, this ecosystem is laying the foundation for enterprise-grade, scalable agentic AI, helping companies automate workflows, drive smarter decisions, and unlock productivity at unprecedented scale.
What Undercode Say:
The rollout of NVIDIA’s AI Data Platform is more than just a technological upgrade — it’s a strategic realignment of AI infrastructure around agentic intelligence. What stands out here is the modular, interoperable nature of the reference design. NVIDIA has cleverly created a blueprint that isn’t bound to any one vendor, allowing hardware manufacturers to innovate while staying compatible with NVIDIA’s software ecosystem.
Let’s break down the significance from a technical and industry perspective:
Interoperability is key. By offering a reference design, NVIDIA ensures seamless integration between AI hardware and software layers, reducing vendor lock-in and accelerating time to deployment.
RAG (Retrieval-Augmented Generation) is no longer a buzzword — it’s becoming the bedrock for enterprise AI. With NeMo Retriever and AI-Q, enterprises can instantly extract insights from large datasets, turning passive information into interactive knowledge.
Security and compliance are built into the infrastructure. Embedding compute closer to data (thanks to GPUs and DPUs near storage) allows for faster, more secure AI inference, vital for industries dealing with sensitive data.
Taiwan emerges as a crucial innovation hub. With ODMs like Quanta, Wistron, and Foxconn leading hardware design, the region plays a critical role in delivering scalable, energy-efficient solutions.
VAST Data’s vision stands out. Their aim to build intelligent, multi-agent systems hints at the next phase of AI — not just isolated agents but entire networks of collaborating AI units capable of collective reasoning.
IBM Fusion’s hybrid cloud model offers a bridge between traditional enterprise infrastructure and cutting-edge AI, showing that legacy systems don’t have to be left behind in the AI race.
collaboration is a watershed moment in enterprise AI infrastructure. It lays down the groundwork for companies to evolve from data-rich to insight-rich, and eventually to agentic intelligence-driven enterprises.
Fact Checker Results ✅
✅ Multiple verified vendors like IBM, NetApp, and Dell are actively deploying AI Data Platform-based products.
✅ NVIDIA’s AI Data Platform includes real technologies like NeMo Retriever and BlueField DPUs.
✅ ODM partners mentioned (ASUS, Foxconn, Supermicro, etc.) are confirmed to be developing compatible AI hardware.
Prediction 🔮
Over the next 12–18 months, expect a massive wave of enterprise investment in agentic AI infrastructure. Companies will increasingly prioritize platforms that support RAG and real-time document processing. NVIDIA’s reference model is likely to become the industry standard, much like CUDA did for GPU computing. Taiwan will solidify its role as the hardware backbone of the AI era, while software partners develop even more advanced reasoning agents to work on these optimized systems.
References:
Reported By: blogs.nvidia.com
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