Tech • IA • Crypto
Powering Agentic AI with AI-Ready Data Platforms That Turn Data Into Intelligence
7/10TL;DR
NVIDIA outlined a shift in enterprise storage toward an “AI data platform” designed to support agentic AI, emphasizing real-time data preparation, new context storage layers, and tighter integration with compute and networking.
KEY POINTS
Data Becomes Central to AI Success
AI performance increasingly depends on the quality and accessibility of enterprise data. Many AI projects fail to move beyond proof of concept due to poor data readiness, reinforcing the principle that models are only as effective as the data they are trained and grounded on.
Three Data Types Reshape Enterprise Architecture
Enterprises now manage structured, unstructured, and a new category: agent context. While structured data drives core business decisions and unstructured data dominates volume, agent-generated context introduces persistent, evolving memory that must be stored and managed long term.
From Stateless Systems to Continuous Agent Workflows
Traditional systems handled short-lived, stateless interactions. Agentic AI introduces long-running processes, where tasks continue autonomously and data remains active 24/7. This shift increases demands on storage for scale, performance, and continuous availability.
Storage Evolves Into a “System of Context”
Storage is transitioning from a passive system of record to an active system of context. It must now support real-time data ingestion, blending structured and unstructured inputs, and enabling thousands of agents to access and process data simultaneously.
AI Data Platform Concept Emerges
NVIDIA and partners are promoting the AI data platform, which prepares data at the moment of creation. This includes ingestion, cleansing, enrichment, chunking, embedding into vector databases, indexing, and governance, all integrated directly into storage systems.
Data Preparation Challenges and Risks
Preparing data for AI is complex due to multimodal formats, duplication, privacy concerns, and constant change. Failures in data handling have led to high-profile issues, including hallucinated policies and costly legal disputes, highlighting that many AI failures are fundamentally data problems.
Real-Time Processing and Governance Are Critical
Enterprises must process and index data continuously, not in batches. At the same time, strict requirements for security, compliance, and access control remain essential, particularly in hybrid environments spanning cloud and on-premises systems.
New Hardware and Infrastructure Integration
The introduction of Vera and BlueField-4 STX platforms aims to accelerate data movement and processing. These systems improve memory bandwidth and single-thread performance, benefiting both GPUs and storage workloads in high-performance AI environments.
Blueprints for Simplifying AI Data Pipelines
NVIDIA is developing reference architectures such as RAG (retrieval-augmented generation) and Video Search and Summarization. These enable storage systems to handle vectorization, embeddings, and multimodal understanding directly, reducing complexity for enterprises.
Partner Ecosystem Adoption
Major vendors including Dell, NetApp, and IBM are integrating these capabilities into their storage offerings. This signals a shift from theoretical frameworks to deployable, production-ready AI data platforms embedded within enterprise infrastructure.
New Memory Hierarchy for AI Workloads
A multi-tier memory model spans GPU memory (HBM), system memory, local NVMe, and networked storage. A new intermediate tier, sometimes described as G3.5, is introduced to efficiently handle KV cache, reducing recomputation and improving inference performance.
KV Cache as a Performance Multiplier
Efficient KV cache storage allows GPUs to reuse prior computations, significantly increasing token throughput and reducing latency. This layer balances speed and cost, sitting between high-speed memory and traditional storage.
Co-Design Across the Stack
The evolving architecture requires tight integration across compute, networking, and storage. Technologies like Spectrum-X networking and GPU acceleration are combined with storage innovations to meet the demands of large-scale agentic AI systems.
CONCLUSION
Enterprise storage is being fundamentally redesigned to support agentic AI, evolving into an integrated AI data platform that prepares, governs, and delivers context-rich data in real time.