Driving the Future of AI Data Readiness: Introducing Everpure Data Stream

We are pleased to announce a significant advance in our efforts to simplify data for AI consumption and accelerate inference at scale using Everpure Data Stream, a key component of Everpure AI Data Platform.

Data Stream accelerates data readiness to directly address the data readiness crisis for enterprise AI initiatives. 

Data Stream is a GPU-centric AI-powered, integrated hardware and software stack built for data readiness to automate and accelerate the ingestion, transformation, and optimization of data for enterprise AI pipelines. It is a core component of the Everpure Data Platform built for enterprise inference use cases, using the NVIDIA AI Data Platformreference design, a comprehensive, single-appliance, jointly engineered infrastructure stack tailored for AI inference via generative AI applications.

Organizations will be able to transform unstructured raw data into actionable, real-time intelligence with unprecedented efficiency—and a single SKU for hassle-free procurement and implementation.

The NVIDIA AI Data Platform reference design is a robust foundation for AI, featuring NVIDIA accelerated computing, NVIDIA networking, NVIDIA AI Enterprise software, and scalable storage solutions. Data Stream uniquely serves as the intelligent orchestration layer, ensuring data is rendered AI-ready through automated GPU-accelerated processing and GPU-aligned delivery.

The Core Component of Everpure AI Data Platform: Data Stream

In AI development cycles—spanning large language model (LLM) retrieval-augmented generation (RAG) systems and real-time copilot implementations—data readiness remains a primary constraint. Enterprises often allocate up to 80% of their AI project timelines to tasks such as data ingestion, cleansing, curation, semantic tagging, and format conversion (i.e., indexing and vectorizing). Data Stream addresses these challenges by embedding data pipeline automation directly into the AI fabric that connects the storage and GPU.

Key technical capabilities include:

  • Automated real-time data ingestion and structuring: Data Stream ingests raw data from diverse sources, including text documents, PDFs, images, and structured tables. It performs intelligent chunking and transformation—dividing content into semantically coherent segments such as sentences or paragraphs—to preserve contextual integrity and granular access control while minimizing information loss. This process supports multiprotocol access (NFS, S3, SMB) and handles billions of files or objects, enabling seamless integration with built-in vector databases for scalable storage on Everpure® FlashBlade//S™.

  • Seamless NVIDIA NeMo integration: Leveraging NVIDIA NeMo Retriever, Data Stream orchestrates end-to-end workflows from data readiness to model inference. NeMo Retriever enables GPU-accelerated vector embedding generation, where raw chunks of data are transformed into high-dimensional semantic vectors using custom and efficient embedding models from NVIDIA. These embeddings facilitate advanced similarity searches via approximate nearest neighbor (ANN) algorithms, HNSW, IVF, and others, ensuring precise retrieval in RAG pipelines. The integration supports NVIDIA NIM for deploying optimized inference, allowing seamless scaling across on-premises or cloud environments via standardized APIs.

  • GPU-optimized pipeline acceleration: Leveraging the compute power of the NVIDIA RTX PRO(™) 6000 Blackwell Server Edition GPU and various software libraries like NVIDIA Spark Rapids & NVIDIA cuVS, the GPU-optimized Pipeline Acceleration ensures data delivery is precisely synchronized with its processing cycles. This architecture utilizes NVIDIA ConnectX-7 NICs for low-latency networked storage access. This synchronization prevents compute bottlenecks in the RTX PRO Server, delivering significant performance gains in vector ingestion when paired with FlashBlade//S. Orchestration occurs at the storage layer, where transformations such as metadata enrichment and reranking for relevance are executed in parallel, substantially reducing end-to-end latency for inference.

  • Storage-layer transformation and enrichment: By processing enrichments natively on FlashBlade® DirectFlash® Modules, which leverage non-volatile RAM (NVRAM) for global metadata management, Data Stream minimizes data movement overhead. Outputs are formatted in efficient structures like JSON, Apache Parquet, or Arrow, unlocking additional capacity in vector stores. This approach supports petabyte-scale RAG data sets, with independent scaling of capacity and performance to accommodate multiple GPU clusters without downtime. Overall, Data Stream maximizes the performance per dollar for inference to empower our customers through various hardware and software innovations.

In short: Data Stream represents a turbocharger for AI-ready data consumption in the enterprise. It allows businesses to accelerate outcomes without fear of long wait times, complexity, and broken pipelines. Through our deep integration of NVIDIA NeMo, Everpure dramatically reduces the latency and complexity of data usability for AI applications. This enables instantaneous access to transformed, vectorized data that is inherently optimized for GPU-centric architectures, which means more inference and consumption without the hassle or complexity.

A Collaboration Driving Innovation

Our alliance has been pivotal in engineering these capabilities. The synergy—high-performance, energy-efficient storage from Everpure plus advancements in accelerated computing and AI frameworks from NVIDIA—powers the ability of AI Data Platform to handle demanding workloads, from embedding generation to scalable inference.

“The era of AI demands a data platform that can transform vast, unstructured information into real-time intelligence. Everpure leverages the NVIDIA AI Data Platform reference design to supercharge AI reasoning and agents with AI-ready storage infrastructure featuring full-stack NVIDIA accelerated computing, networking, and AI software.”

–Justin Boitano, Vice President of Enterprise AI at NVIDIA

Realizing Transformative AI Outcomes

Data Stream redefines data readiness as an intrinsic, automated function of the AI infrastructure stack. By natively embedding NVIDIA NeMo, it provides a unified, GPU-optimized pathway from raw ingestion to model deployment, mitigating friction points and expediting time to value. Features such as intelligent query augmentation—where user inputs are vectorized and matched against billions of embeddings—and guardrail filtering enhance LLM accuracy, relevance, and security by leveraging retrieved contexts to mitigate hallucinations or inappropriate outputs.

For RAG implementations, this translates to processing diverse enterprise content (e.g., emails, knowledge bases, multimedia) into indexed vectors with built-in traceability. Fine-tuning workflows benefit from reduced data readiness cycles, compressing timelines from weeks to hours. 

At Everpure, we are committed to eradicating inefficiencies at the data-intelligence nexus. Data Stream marks a critical evolution in this pursuit, laying the groundwork for broader AI adoption. We invite enterprise leaders to explore integration opportunities and preview Everpure Data Stream to harness these capabilities in your environments.

Get started today by registering here. 

FAQ

Everpure Data Stream is a GPU‑centric, AI‑powered application that sits between your storage and GPUs to automate and accelerate how enterprise data is ingested, transformed, and optimized for AI pipelines, especially inference and generative AI use cases. It’s delivered as part of the Everpure AI Data Platform, using the NVIDIA AI Data Platform reference design so you get a single, jointly engineered stack rather than assembling infrastructure yourself.

In most AI projects, teams burn the majority of time on unglamorous work: pulling data from scattered systems, cleaning and curating it, tagging it, and converting it into embeddings or other AI‑ready formats. Data Stream turns that into an intrinsic function of the infrastructure by embedding data‑pipeline automation directly into the “fabric” that connects Pure storage and NVIDIA GPUs, so data readiness stops being a one‑off project and becomes a repeatable capability.

Data Stream can pull in raw data from sources like documents, PDFs, images, and structured tables over NFS, S3, and SMB, at the scale of billions of files or objects. It then performs intelligent chunking into semantically coherent units (for example, sentences or paragraphs) and enriches them with metadata before generating vector embeddings and feeding them into built‑in vector databases on FlashBlade//S. The enriched outputs are written in compact formats such as JSON, Parquet, or Arrow so downstream RAG systems and vector stores can search and serve them efficiently at petabyte scale.