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Hari Kannan, Boris Feigin, and Robert Alvarez presented for Pure Storage at AI Data Infrastructure Field Day 1 |
This Presentation date is October 3, 2024 at 14:00-15:30.
Presenters: Boris Feigin, Hari Kannan, Robert Alvarez
Simplify and Accelerate AI Adoption with Pure Storage Platform – FlashBlade Overview
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In this presentation, Pure Storage outlines its approach to help organizations meet their AI storage needs while accelerating and simplifying adoption of their AI initiatives. With the Pure Storage platform, organizations can maximize performance and efficiency of AI workflows, unify data, simplify data storage management and take advantage of a scalable AI data infrastructure.
An essential cornerstone to it is Pure FlashBlade, a powerful scale-out storage solution specifically designed to meet the unique demands of AI workloads. With parallel data architecture and multi-dimensional performance, FlashBlade ensures minimal latency and high bandwidth, to accelerate model training and reduce time to AI results.
This section introduces FlashBlade’s design tenets that make it so well suited for AI projects, and deep dives into the technology that enables Pure’s industry-leading energy efficiency and helps customers overcome data center power constraints in their AI build-outs. The discussion in this section also includes Pure’s DirectFlash module which communicates directly with raw flash to enable greater control, optimized performance, and reduced latency.
Presented by Hari Kannan – Lead Principal Technologist, Pure Storage
Personnel: Hari Kannan
Simplify and Accelerate AI Adoption with Pure Storage Platform – FlashBlade Internals
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In this presentation, Pure Storage outlines its approach to help organizations meet their AI storage needs while accelerating and simplifying adoption of their AI initiatives. With the Pure Storage platform, organizations can maximize performance and efficiency of AI workflows, unify data, simplify data storage management and take advantage of a scalable AI data infrastructure. An essential cornerstone to it is Pure FlashBlade, a powerful scale-out storage solution specifically designed to meet the unique demands of AI workloads. It simplifies the integration and deployment of all scales of training and inference routines, and helps democratize AI to enterprises looking to accelerate their AI initiatives.
This section looks at the internal details of FlashBlade and Purity//FB, the software stack for FlashBlade. This part of the session focuses on the building blocks as well as the core architectural decisions that enable FlashBlade to shine in modern AI environments across many use cases. Central to Purity//FB’s success is its ability to handle massively parallel data processing, which is a crucial AI data infrastructure requirement for large-scale datasets. This part of the session will cover the modularThis section looks at the internal details of FlashBlade and Purity//FB, the software stack for FlashBlade. This part of the session focuses on the building blocks as well as the core architectural decisions that enable FlashBlade to shine in modern AI environments across many use cases. Central to Purity//FB’s success is its ability to handle massively parallel data processing, which is a crucial AI data infrastructure requirement for large-scale datasets. This part of the session will cover the modular design of Purity//FB, illustrating how its distributed architecture efficiently manages data flow.
Presented by Boris Feigin – Technical Director, FlashBlade Engineering, Pure Storage
Personnel: Boris Feigin
Simplify and Accelerate AI Adoption with Pure Storage Platform – Real-World Insights & Use Cases
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In this presentation, Pure Storage outlines its approach to help organizations meet their AI storage needs while accelerating and simplifying adoption of their AI initiatives. With the Pure Storage platform, organizations can maximize performance and efficiency of AI workflows, unify data, simplify data storage management and take advantage of a scalable AI data infrastructure.
Scaling AI workloads—including large language models (LLMs), retrieval augmented generation (RAG) pipelines, and computer vision applications—introduces practical challenges that extend far beyond theoretical storage capabilities. AI environments require vast amounts of data, and as models grow in size and complexity, traditional storage systems can struggle to keep up with the demands of AI training and inference. Based on real-world customer experiences, this presentation shares valuable insights into building efficient, scalable, and reliable storage infrastructures to support these complex AI pipelines. We’ll explore how organizations can address storage bottlenecks and optimize their systems to ensure seamless AI operations at an enterprise level.
Presented by Robert Alvarez – Consulting Solutions Architect, Pure Storage
Personnel: Robert Alvarez