At last week’s GTC conference, Pure Storage, an industry leader in providing cutting-edge data storage services and technologies, introduced a collection of novel AI infrastructure solutions and verified reference architectures that were jointly designed and developed with Nvidia. With the announcement, established frameworks for managing the high-performance compute and data requirements essential for successful AI deployments were intended to be made available to enterprises.
Pure Storage’s Global Practice Leader for AI and Analytics, Miroslav Klivansky, recently spoke with VentureBeat regarding the potential ramifications of the organization’s enhanced collaboration with Nvidia on enterprise adoption of artificial intelligence.
At a critical juncture, organizations from diverse sectors are progressively embracing artificial intelligence (AI) to foster innovation, streamline processes, and attain a competitive edge. Nevertheless, the majority of AI implementations are presently dispersed throughout fragmented data environments, a circumstance that may impede the complete actualization of AI’s capabilities. The partnership between Pure Storage and Nvidia aims to confront this challenge directly.
“Pure Storage and Nvidia have maintained a longstanding partnership,” Klivansky stated to VentureBeat. “We introduced AIRI, the first AI-ready infrastructure reference architecture, in partnership with Nvidia in 2018. Its sole purpose was to enable organizations to scale their AI investments without complexity and to achieve greater utilization and reliability. Pure Storage was subsequently one of the initial providers of enterprise data storage to obtain Nvidia DGX BasePOD certification.
Authorized Reference Architectures That Simplify The AI Conundrum
Klivansky underscored the significance of their ongoing collaboration with Nvidia in support of enterprise AI deployments, stating, “Today’s announcement serves as confirmation of this collaborative partnership.” Pure Storage, an industry leader in artificial intelligence, is collaborating with Nvidia to provide global clients with tried-and-true solutions that meet their high-performance compute and data needs. These solutions enable successful AI deployments by facilitating faster model training and inference, enhancing operational efficiency, and reducing total cost and energy consumption.
A Retrieval Augmented Generation (RAG) Pipeline for AI Inference, which integrates Nvidia GPUs for computation and Pure Storage’s all-flash enterprise storage, was among the primary solutions showcased at GTC. Klivansky explained, “By utilizing Pure Storage’s data storage infrastructure for retrieval-augmented generation, clients can augment standard large language models (LLMs) with proprietary corporate data in order to furnish chatbots with increased relevance, precision, and currency, all while circumventing the substantial financial commitment associated with constructing their own custom large language models.”
Nvidia OVX Server Storage Certification Broadens Options
An additional noteworthy advancement disclosed at GTC was the attainment of Nvidia OVX Server Storage certification by Pure Storage. This accreditation furnishes clients with adaptable storage reference architectures that have been verified against critical benchmarks, thereby guaranteeing a strong infrastructure underpinning for AI hardware and software solutions that are optimized in terms of cost and performance.
Klivansky stated, “With the most recent Nvidia OVX Server Storage validation, we can now offer flexible storage reference architectures to enterprise customers and channel partners. These architectures have been validated against key benchmarks to provide a solid infrastructure foundation for AI hardware and software solutions that are optimized for cost and performance.” “Because OVX computing platforms are powered by Nvidia L40s GPUs, our clients now have a wider selection of validated reference architectures from Pure Storage, as well as more options and availability for compute GPU platforms.”
Klivansky emphasized the distinctive benefits of Pure Storage’s solution, stating, “Pure’s data storage platform, which is optimized for AI applications, distinguishes Pure’s solution from those of its competitors. “In addition to exceptional performance, operational efficiency, energy and cost savings, and a dependable, future-proof storage foundation that expands to accommodate evolving AI data requirements, Pure’s FlashBlade//S…”
AI-Specific Vertical Solutions Beginning With Finance
Alongside their broader infrastructure solutions for artificial intelligence, Pure Storage and Nvidia are concurrently dedicating efforts to the development of AI applications that are specific to various verticals. Initially, there is a Financial RAG that has been developed exclusively for the finance sector, utilizing FinGPT.
Klivansky emphasized the prevalence of AI use cases specific to the finance industry in large organizations, stating, “Generative AI employing retrieval augmented generation (RAG) and pre-trained LLMs can generate financial analyses that surpass human-generated ones in terms of accuracy, real-time speed, and cost-effectiveness. This is due to the significantly greater information processing capacity and velocity at which these algorithms operate.”
Pure Storage and Nvidia’s dedication to catering to the distinct requirements of various sectors is exemplified through the creation of AI solutions focused on specific verticals. The companies endeavor to boost the uptake of AI in industries including finance, healthcare, and the public sector through the provision of customized AI applications.
Enhancing The Ecosystem Of AI Partners
Pure Storage’s commitment to fulfilling the dynamic data storage requirements of artificial intelligence is additionally substantiated by the growth of its partner ecosystem. Channel partners including WWT, ePlus, CDW, and Insight, in addition to prominent AI platform providers such as Run.AI and Weights & Biases, have formed strategic alliances with the organization.
Klivansky stated, “We are thrilled to increase investment in our AI partner ecosystem, which includes Run.AI and Weights & Biases, among others.” “By optimizing the utilization of compute resources powered by GPUs to expedite development, Run.AI enhances the storage performance and energy efficiency value of Pure’s AI infrastructure.” Weights & Biases enhances Pure’s data storage infrastructure for AI by offering a user-friendly framework for model construction and RAG pipelines. This further improves operational efficiency and expedites end-to-end ML workflows.
By virtue of its partnerships, Pure Storage is capable of providing an all-encompassing and cohesive AI solution stack, thereby streamlining the implementation and administration of AI infrastructure for large organizations. Through the utilization of these partners’ specialized knowledge and skills, Pure Storage is capable of providing an AI experience that is both streamlined and optimized, encompassing data storage, model development, and deployment.
Sustainable AI Enabled By Energy-Efficient Storage
Concerns have arisen regarding the environmental impact of data centers in tandem with the expansion of AI adoption. In response to this challenge, Pure Storage has developed a storage platform that assists clients in minimizing their carbon footprint and energy usage.
Klivansky explained, “Pure has designed and constructed its platform to enable customers to substantially reduce their energy and carbon footprints.” “On average, the flash-optimized systems from Pure Storage consume two to five times less power than SSD-based systems offered by competitors, and between five and ten times less power than the hard disk systems that we replace.”
Pure Storage’s provision of energy-efficient storage solutions empowers organizations to construct AI infrastructure that is more environmentally sustainable. This not only facilitates the achievement of environmental objectives for organizations but also yields substantial financial benefits in relation to power, refrigeration, and data center space.
Establishing A Foundation For The Future Of Enterprise AI
According to Klivansky, “Pure Storage predicted the demands and possibilities of artificial intelligence at the outset of this most recent surge, providing the sector with a container-ready, high-performing, and efficient storage infrastructure necessary to exploit the enormous volumes of data that power this technology and genuinely extract value from them.”
Pure Storage, founded on its dedication to innovation and a history of successfully delivering sophisticated data storage solutions, is exceptionally positioned to assume a crucial role in the ongoing AI revolution. The organization’s unwavering commitment to fulfilling the distinct data storage needs of artificial intelligence duties is apparent in its ongoing endeavors to establish strategic alliances and innovate revolutionary solutions.
COM ENTREMENT
“In recent years, Pure Storage has been a pioneer in developing solutions to address the increasing demands for data storage in AI deployments,” stated Klivansky. “In response to the difficulties presented by disparate data environments, Pure Storage was at the forefront of developing an enterprise data storage platform tailored to address the specific requirements of artificial intelligence.” Through the provision of a straightforward, dependable, and effective storage infrastructure, Pure Storage empowers organizations to effectively leverage the capabilities of artificial intelligence (AI) while concurrently minimizing expenses, risks, and energy usage.
The partnership between Pure Storage and Nvidia serves as evidence of their mutual understanding regarding the trajectory of artificial intelligence and their dedication to empowering organizations to harness the complete capabilities of this paradigm-shifting technology. Through the provision of a validated and all-encompassing AI infrastructure solution, these companies aid businesses in diverse sectors in expediting their adoption of AI and attaining a competitive advantage in a world that is becoming increasingly data-centric.