NVIDIA’s generative AI infrastructure finds its technical heartbeat within a finely orchestrated set of inference tooling known collectively as NVIDIA NIM.
This elegant bit of engineering consolidates inference and modeling into a neat microservice architecture, which serves as the central node around which enterprise-class GenAI applications orbit.
In the case of Retrieval Augmented Generation (RAG), NVIDIA’s collection of NIM microservices for information retrieval, called NVIDIA NeMo Retriever, power nuanced interactions between vast amounts of data and the valuable retrieval tasks modern enterprises require.
The NeMo Retriever collection of NIM microservices is used to build enterprise-focused retrieval engines that balance accuracy, throughput, and latency—the computational trifecta needed for meaningful AI at scale.
The NeMo Retriever modular design, present along the data extraction and embedding steps through vector-database storage and final insight generation, allows customized workflows that quietly adapt to specific enterprise data ecosystems, thus going far beyond what traditional enterprise analytics could ever provide.
Likewise, NVIDIA NeMo Retriever Embedding and reranking NIM ensure that retrieval tasks are efficient and precise, translating unwieldy data into structured, instantly retrievable vectors, thereby streamlining that which has been traditionally burdened by sheer volume and complexity.
The secret sauce is NVIDIA’s accelerated computing optimization for vector databases, achieved through the NVIDIA cuVS open-source library, which cuts through traditional indexing bottlenecks.
By coupling these accelerated operations with embedding models, throughput dramatically increases, turning once heavy-lifting-required retrieval efforts into what can only be described as elegantly efficient procedures.
The handling of multimodal data types - think PDFs, images, and presentations - is also transformed via dedicated models that can parse individual data elements, carving out precise insights while ensuring the clarity and completeness of enterprise data.
This foundation laid by NVIDIA paves the way for the end-to-end GPU-optimized AI Blueprint for RAG, a comprehensive and adaptable pipeline that integrates high-speed multimodal PDF data extraction, embedding and reranking technologies, accelerated vector search, large language models, and carefully engineered NeMo guardrails that can be deployed on infrastructure no matter where it sits.
These abstractions gain true practical relevance—and a certain undeniable clarity—in the hands of VAST Data.
At GTC 2025, VAST VP of Architecture Sagi Grimberg highlighted how it all comes together. Shifting the conversation away from purely theoretical frameworks, Grimberg highlighted some concrete enterprise scenarios showing the value in combining NVIDIA’s GenAI components with VAST Data’s platform.
Take a look at VAST’s RAG pipeline, emphasizing the all-important deeply integrated serverless runtime.
Documents, presentations, video, the list goes on…all ingested seamlessly into VAST’s data store— automatically triggers the runtime, activating data extraction functions to parse text, objects, tables, and graphics.
Each ingested file undergoes precise chunking, and textual segments are transformed into vector embeddings via NVIDIA NeMo Retriever models, which then populate VAST’s unified vector database. Metadata and more nuanced content attributes (charts, diagrams, structured tables) are captured and stored simultaneously, ensuring no critical context is lost.
The retrieval mechanism Grimberg shows off here employs some pretty sophisticated conversational context management that’s worth zooming in on.
Imagine a user interaction—let’s say a query via a prompt-based interface. The system invokes NeMo Retriever to generate the requisite contextual embeddings, executing vector similarity searches across vast data repositories. Those results are further refined through re-ranking processes using NeMo Retriever, ensuring only the most relevant data surfaces.
And here’s where things get more interesting: retrieval is strictly gated by a dynamically updated, unified permissions layer, reflecting real-time user groups and identity policies.
Grimberg highlighted this via a hypothetical enterprise user, Adam. Permissions updates instantly impact Adam’s accessible data, underscoring how granular access controls integrate seamlessly into automated pipeline operations.
Further showcasing the power of the platform, the crowd was also treated to a sophisticated vision-based AI pipeline for interpreting and enhancing video content.
Here, the pipeline begins with ingestion into VAST’s storage, where video content is segmented automatically into manageable clips via VAST’s runtime functions. These clips undergo detailed analysis through NVIDIA’s powerful Llama Nemotron 34-billion-parameter models, which interpret visual content, annotating each segment with descriptive metadata.
Subsequent pipeline stages deploy specialized generative AI agents to puff out the metadata contextually, meaning it’s possible to apply customized lenses or domain-specific reasoning to the video content.
Subject matter aside, the demo emphasizes the practical applicability of this pipeline: users query extensive video archives effortlessly, retrieving precise segments (such as “Bugs Bunny eating carrots”) instantly, powered by comprehensive clip-level annotations.
Additionally, these generative agents support nuanced reasoning tasks. Grimberg prompting an agent to explore Bugs Bunny’s carrot preferences uncovers a subtle yet insightful conclusion: Bugs’ affinity for carrots may indeed be more branding-driven than inherently genuine—a playful but potent example of the system’s interpretive intelligence.
Ultimately, what NVIDIA and VAST have engineered here is less a mere incremental advance in enterprise AI than a kind of architectural leap—one that can redefine what we might reasonably expect from real-time inference and retrieval systems at scale.
By threading NVIDIA’s GPU-optimized inference microservices and modular retrieval architectures through VAST’s serverless runtime and permissioning layers, the partnership achieves a sort of technical alchemy: transforming unruly, multimodal enterprise data into something that is seamlessly, coherent, searchable, and perhaps most important, powerfully context-aware.
