VAST Data is integrating Nvidia’s AI-Q AI agent building and connecting blueprints into its storage to help its customers enter the AI agent-building rat race.
AI-Q is a blueprint or reference design plan for integrating Nvidia GPUs, partner storage platforms, and software and Agent Intelligence Toolkit when developing AI agents. It includes Nvidia’s Llama Nemotron reasoning models, NeMO retriever and NIM microservices. The Agent Intelligence Toolkit, available on GitHub, is an open-source software library for connecting, profiling and optimizing teams of AI agents. It integrates with frameworks and tools like CrewAI, LangGraph, Llama Stack, Microsoft Azure AI Agent Service and Letta. A storage partner, such as VAST, continually processes data so that connected agents can respond to data changes, reasoning and acting on them.
Jeff Denworth, Co-Founder at VAST Data, stated: “The agentic era is going to challenge every assumption about the scale, performance, and value of legacy infrastructure. As enterprises race to operationalize AI, it’s no longer enough to just build smarter models — those models need immediate, unrestricted access to the data that drives intelligent decision-making. By embedding NVIDIA AI-Q into a platform purpose-built for the AI era, we’re delivering the scalable, high-performance data platform required to power the next generation of enterprise AI; one driven by real-time, multimodal intelligence, continuous learning, and dynamic agentic pipelines.”
VAST Data says the AI-Q blueprint “povides an environment for rapid metadata extraction and establishes heterogeneous connectivity between agents, tools, and data, simplifying the creation and operationalization of agentic AI query engines that reason across structured and unstructured data with transparency and traceability.”
Within the AI-Q environment, the VAST Data storage and AI engine component data stack is a secure, AI-native pipeline that takes raw data and transforms and feeds it upstream to AI Agents. As part of that Nemo Retriever is used by VAST and AI-Q to extract, embed, and rerank relevant data before passing it to advanced language and reasoning models.
VAST plus AI-Q will provide:
- Multi-modal unstructured and semi-structured data RAG including enterprise documents, images, videos, chat logs, PDFs, and external sources like websites, blogs, and market data.
- Structured data with VAST connecting AI agents directly to structured data sources such as ERP, CRM, and data warehouses for real-time access to operational records, business metrics, and transactional systems.
- Fine-grained user and agent access control through policies and security features.
- Real-time agent optimization via Nvidia’s Agent Intelligence Toolkit and VAST’s telemetry.
VAST says it and Nvidia are enabling organizations to build real-time AI intelligence engines to enable teams of AI agents to deliver more accurate responses, automate multi-step tasks, and continuously improve via an AI data flywheel.
Justin Boitano, VP, Enterprise AI at Nvidia, said: “AI-driven data platforms are key to helping enterprises put their data to work to drive sophisticated agentic AI systems. Together, NVIDIA and VAST are creating the next generation of AI infrastructure with powerful AI systems that let enterprises quickly find insights and knowledge stored in their business data.”
VAST’s Jon Mao, VP of Business Development and Alliances, blogs “By leveraging the Nvidia AI-Q blueprint and a powerful suite of Nvidia software technologies — from Nvidia NeMo and Nvidia NIM microservices to Nvidia Dynamo, Nvidia Agent Intelligence toolkit, and more — alongside the VAST InsightEngine and our VUA acceleration layer, this platform empowers enterprises to deploy AI agent systems capable of reasoning over enterprise data, to deliver faster, smarter outcomes. It’s a new kind of enterprise AI stack, built for the era of AI agents — and VAST is proud to be leading the way.”
Read more in Mao’s blog.
Comment
Until recently a storage system only needed to support GPUDirect to deliver data fast to Nvidia’s GPUs. Now it needs to integrate with the Nvidia AI-Q blueprint and continually feed data to Nvidia agents and AI software stack components, which use the GPUs, so as to become an Nvidia AI data platform. We predict more storage suppliers will adopt AI-Q
