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The Pure Report
Pure Storage
262 episodes
2 days ago
In this episode, we welcome Lead Principal Technologist Hari Kannan to cut through the noise and tackle some of the biggest myths surrounding AI data management and the revolutionary FlashBlade//EXA platform. With GPU shipments now outstripping CPUs, the foundation of modern AI is shifting, and legacy storage architectures are struggling to keep up. Hari dives into the implications of this massive GPU consumption, setting the stage for why a new approach is desperately needed for companies driving serious AI initiatives. Hari dismantles three critical myths that hold IT leaders back. First, he discusses how traditional storage is ill-equipped for modern AI's millions of small, concurrent files, where metadata performance is the true bottleneck—a problem FlashBlade//EXA solves with its metadata-data separation and single namespace. Second, he addresses the outdated notion that high-performance AI is file-only, highlighting FlashBlade//EXA's unified, uncompromising delivery of both file and object storage at exabyte scale and peak efficiency. Finally, Hari explains that GPUs are only as good as the data they consume, countering the belief that only raw horsepower matters. FlashBlade//EXA addresses this by delivering reliable, scalable throughput, efficient DirectFlash Modules up to 300 TB, and the metadata performance required to keep expensive GPUs fully utilized and models training faster. Join us as we explore the blind spots in current AI data strategies during our "Hot Takes" segment and recount a favorite FlashBlade success story. Hari closes with a compelling summary of how Pure Storage's complete portfolio is perfectly suited to provide the complementary data management essential for scaling AI. Tune in to discover why FlashBlade//EXA is the non-compromise, exabyte-scale solution built to keep your AI infrastructure running at its full potential. For more information, visit: https://www.pure.ai/flashblade-exa.html Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 04:30 Primer on FlashBlade 11:32 Stat of the Episode on GPU Shipments 13:25 What is FlashBlade//EXA 18:58 Myth #1: Traditional Storage Challenges for AI Data 22:01 Myth #2: AI Workloads are not just File-based 26:42: Myth #3: AI Needs more than just GPUs 31:35 Hot Takes Segment
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In this episode, we welcome Lead Principal Technologist Hari Kannan to cut through the noise and tackle some of the biggest myths surrounding AI data management and the revolutionary FlashBlade//EXA platform. With GPU shipments now outstripping CPUs, the foundation of modern AI is shifting, and legacy storage architectures are struggling to keep up. Hari dives into the implications of this massive GPU consumption, setting the stage for why a new approach is desperately needed for companies driving serious AI initiatives. Hari dismantles three critical myths that hold IT leaders back. First, he discusses how traditional storage is ill-equipped for modern AI's millions of small, concurrent files, where metadata performance is the true bottleneck—a problem FlashBlade//EXA solves with its metadata-data separation and single namespace. Second, he addresses the outdated notion that high-performance AI is file-only, highlighting FlashBlade//EXA's unified, uncompromising delivery of both file and object storage at exabyte scale and peak efficiency. Finally, Hari explains that GPUs are only as good as the data they consume, countering the belief that only raw horsepower matters. FlashBlade//EXA addresses this by delivering reliable, scalable throughput, efficient DirectFlash Modules up to 300 TB, and the metadata performance required to keep expensive GPUs fully utilized and models training faster. Join us as we explore the blind spots in current AI data strategies during our "Hot Takes" segment and recount a favorite FlashBlade success story. Hari closes with a compelling summary of how Pure Storage's complete portfolio is perfectly suited to provide the complementary data management essential for scaling AI. Tune in to discover why FlashBlade//EXA is the non-compromise, exabyte-scale solution built to keep your AI infrastructure running at its full potential. For more information, visit: https://www.pure.ai/flashblade-exa.html Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 04:30 Primer on FlashBlade 11:32 Stat of the Episode on GPU Shipments 13:25 What is FlashBlade//EXA 18:58 Myth #1: Traditional Storage Challenges for AI Data 22:01 Myth #2: AI Workloads are not just File-based 26:42: Myth #3: AI Needs more than just GPUs 31:35 Hot Takes Segment
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Technology
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15 Architectural Decisions Series: Snapshots, Realistic Efficiency Metrics, and Adaptive RAID
The Pure Report
34 minutes 32 seconds
3 months ago
15 Architectural Decisions Series: Snapshots, Realistic Efficiency Metrics, and Adaptive RAID
Episode 4 of our series around Pure's fundamental design principles dives into aspects of Pure's technology that drive realistic efficiency for users. This episode tackles capabilities most Storage professionals are familiar with - snapshots, efficiency metrics and adaptive flexible RAID. Pure learned from the challenges created by legacy architectures to build better versions of these features into our all-flash array portfolio. Hear about how Pure Storage’s snapshot architecture focuses on instant, efficient, and immutable copies that serve both operational and security needs. Snapshots can be created instantly with no performance overhead and used for rapid recovery, cloning, or replication—each one functions as a “gold copy version” and maintains group consistency, enabling rollbacks to any point in time without dependencies or data duplication. Their design is tightly integrated with metadata, enabling efficient thin provisioning and consistent protection, while SafeMode extends these benefits by enforcing privileged access and retention policies for ransomware resilience. Around realistic efficiency metrics - we deliver transparent and realistic efficiency metrics by directly reporting space consumption trends and physical storage utilization, rather than relying solely on claims about thin provisioning or data reduction. Our user interface distinguishes between budgeted capacity, usable capacity, and used space, with a focus on showing only live user data (volumes and snapshots), and hiding internal system overhead, RAID overhead, and reclaimable space until critical thresholds are reached. Finally, we tackle RAID - Pure’s RAID architecture is built for flash, utilizing a flexible, adaptive structure that never requires manual configuration while continuously ensuring optimal data protection and performance. Series Overview: Pure Storage’s foundational approach to product engineering is guided by 15 architectural decisions that were established at the company’s inception and have shaped both the technical and user experience across its product lines. These architectural choices were not made arbitrarily—they stem from a deliberate focus on simplicity, efficiency, and scalability, ensuring Pure could deliver storage solutions that break away from legacy complexity and enable continuous innovation without compromise. This series will guide viewers through all of the 15 principles, helping you understand why certain choices were made, how they impact your operations, and how they compare to other industry features and products. Join Pure Report podcast hosts Rob Ludeman, Andrew Miller, and J.D Wallace for this fun technical retrospective on Pure Storage.
The Pure Report
In this episode, we welcome Lead Principal Technologist Hari Kannan to cut through the noise and tackle some of the biggest myths surrounding AI data management and the revolutionary FlashBlade//EXA platform. With GPU shipments now outstripping CPUs, the foundation of modern AI is shifting, and legacy storage architectures are struggling to keep up. Hari dives into the implications of this massive GPU consumption, setting the stage for why a new approach is desperately needed for companies driving serious AI initiatives. Hari dismantles three critical myths that hold IT leaders back. First, he discusses how traditional storage is ill-equipped for modern AI's millions of small, concurrent files, where metadata performance is the true bottleneck—a problem FlashBlade//EXA solves with its metadata-data separation and single namespace. Second, he addresses the outdated notion that high-performance AI is file-only, highlighting FlashBlade//EXA's unified, uncompromising delivery of both file and object storage at exabyte scale and peak efficiency. Finally, Hari explains that GPUs are only as good as the data they consume, countering the belief that only raw horsepower matters. FlashBlade//EXA addresses this by delivering reliable, scalable throughput, efficient DirectFlash Modules up to 300 TB, and the metadata performance required to keep expensive GPUs fully utilized and models training faster. Join us as we explore the blind spots in current AI data strategies during our "Hot Takes" segment and recount a favorite FlashBlade success story. Hari closes with a compelling summary of how Pure Storage's complete portfolio is perfectly suited to provide the complementary data management essential for scaling AI. Tune in to discover why FlashBlade//EXA is the non-compromise, exabyte-scale solution built to keep your AI infrastructure running at its full potential. For more information, visit: https://www.pure.ai/flashblade-exa.html Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 04:30 Primer on FlashBlade 11:32 Stat of the Episode on GPU Shipments 13:25 What is FlashBlade//EXA 18:58 Myth #1: Traditional Storage Challenges for AI Data 22:01 Myth #2: AI Workloads are not just File-based 26:42: Myth #3: AI Needs more than just GPUs 31:35 Hot Takes Segment