Pure Storage announced new validated reference architectures for running generative AI use cases, including a new NVIDIA OVX-ready validated reference architecture. As a leader in AI, Pure Storage, in collaboration with NVIDIA, is arming global customers with a proven framework to manage the high-performance data and compute requirements they need to drive successful AI deployments.
“Embracing our long-standing collaboration with NVIDIA, the latest validated AI reference architectures and generative AI proofs of concept emerge as pivotal components for global enterprises in unraveling the complexities of the AI puzzle.”
Rob Lee, Chief Technology Officer, Pure Storage.
News Highlights:
Building on this collaboration with NVIDIA, Pure Storage delivers the latest technologies to meet the rapidly growing demand for AI across today’s enterprises. New validated designs and proofs of concept include:
- Retrieval-Augmented Generation (RAG) Pipeline for AI Inference: To improve the accuracy, currency, and relevance of Inference capabilities for large language models (LLMs), Pure Storage created a RAG pipeline leveraging NVIDIA NeMo Retriever microservices and NVIDIA GPUs and Pure Storage for all-flash enterprise storage. As a result, Pure Storage accelerates time to insight for enterprises using their own internal data for AI training, ensuring the use of their latest data and eliminating the need for constant retraining of LLMs.
- Certified NVIDIA OVX Server Storage Reference Architecture: Pure Storage has achieved OVX Server Storage validation, providing enterprise customers and channel partners with flexible storage reference architectures, validated against key benchmarks to provide a strong infrastructure foundation for cost- and performance-optimized AI hardware and software solutions. This validation offers additional choice for AI customers and complements Pure Storage’s certification for NVIDIA DGX BasePOD announced last year.
- Vertical RAG Development: To accelerate successful AI adoption across vertical industries, Pure Storage is creating vertical-specific RAGs in collaboration with NVIDIA. First, Pure Storage has created a financial services RAG solution to summarize and query massive datasets with higher accuracy than off-the-shelf LLMs. Financial services institutions can now gain faster insight using AI to create instant summaries and analysis from various financial documents and other sources. Additional RAGs for healthcare and public sector to be released.
- Expanded Investment in AI Partner Ecosystem: Pure Storage is further investing in its AI partner ecosystem with NVIDIA, engaging in new partnerships with ISVs like Run.AI and Weights & Biases. While Run.AI optimizes GPU utilization through advanced orchestration and scheduling, the Weights & Biases AI Development platform enables ML teams to build, evaluate, and govern the model development lifecycle. Additionally, Pure Storage is working closely with AI-focused reseller and service partners including ePlus, Insight, WWT, and others to further operationalize joint customer AI deployments.
“Pure Storage recognized the rising demand for AI early on, delivering an efficient, reliable, and high-performance platform for the most advanced AI deployments. Embracing our long-standing collaboration with NVIDIA, the latest validated AI reference architectures and generative AI proofs of concept emerge as pivotal components for global enterprises in unraveling the complexities of the AI puzzle,” commented Rob Lee, Chief Technology Officer, Pure Storage.
“NVIDIA’s AI platform reference architectures are enhanced by Pure’s simple, efficient, and reliable data infrastructure, delivering comprehensive solutions for enterprises navigating the complexities of AI, data analytics, and advanced computing. Pure’s latest validated reference architectures and proofs of concept are helping drive AI innovation, providing businesses with the tools needed to unlock new possibilities and drive transformative outcomes,” said Bob Pette, Vice President of Enterprise Platforms, NVIDIA.
Industry Significance:
Today, the majority of AI deployments are dispersed across fragmented data environments — from the cloud to ill-suited (often legacy) storage solutions. Yet these fragmented environments cannot support the performance and networking requirements to fuel AI data pipelines and unlock the full potential of enterprise data.
As enterprises further embrace AI to drive innovation, streamline operations, and gain a competitive edge, the demand for robust, high-performance, and efficient AI infrastructure has never been stronger.
Pioneering enterprise AI deployments, particularly among a rapidly growing set of Fortune 500 enterprise customers, Pure Storage provides a simple, reliable, and efficient storage platform for enterprises to fully leverage the potential of AI, while reducing the associated risk, cost, and energy consumption.“
Pure Storage’s NVIDIA-validated reference architectures and proofs of concept provide enterprises across industries a shortcut to AI success. Rather than investing valuable time and resources in building an AI architecture from scratch, Pure’s proven frameworks not only mitigate the risk of expensive project delays but also guarantee a high return on investment for AI team expenditures like GPUs,” said Mike Leone, Principal Analyst, ESG
