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The Storage Architecture Spectrum: Why “Shared-nothing” Means Nothing by Pure Storage Blog This blog on the storage architecture spectrum is Part 2 of a five-part series diving into the claims of new data storage platforms. And just as important, why there is more to any product or platform than just architecture.
As AI progressed, each wave of innovation placed new demands on storage, driving advancements in capacity, speed, and scalability to accommodate increasingly complex models and larger data sets. In addition to checkpointing, emerging architectures like retrieval-augmented generation (RAG) present unique challenges for storage systems.
A recent Gartner reports reveals that by 2025, more than 70% of corporate, enterprise-grade storage capacity will be deployed as consumption-based offerings—up from less than 40% in 2021. . Consumed capacity. SLAs are the legal agreements we make with our customers on measurable metrics like uptime, capacity, and performance.
We also created our own DirectFlash® Modules in 2017 , allowing us to implement 100% of our storage functionality in software. Resource Balancer only uses capacity-free space to determine where to place the new volume.¹³ Item #3: “ Active/Active Controller Architecture”¹⁴ Is a Good Thing We see this B.S. The six PowerStore B.S.
We first started working with NVIDIA back in 2017. Now, we’ve announced our NVIDIA DGX SuperPOD certification to come later this year, and we have validated reference architectures with NVIDIA across the entire spectrum of training with AIRI, NVIDIA DGX BasePOD , and the NVIDIA OVX reference architecture on the inference side.
According to McKinsey , adoption of AI has doubled from 2017 to 2022. This updated architecture disaggregates storage and compute to deliver a highly configurable, customizable file and object storage platform to target many analytics and AI workload profiles. Learn more about how Pure Storage can help your AI initiatives today.
Later generations of Symmetrix were even bigger and supported more drives with more capacity, more data features, and better resiliency. . Today, Pure1 can help any type of administrator manage Pure products while providing VM-level and array-level analytics for performance and capacity planning. HP even shot its array with a.308
FlashBlade provides a high-performance metadata engine to match the high-throughput data delivery for SMB, NFS, and S3 in a simple, scale-out architecture. Both compute/GPUs and storage scale independently and the exact ratio will vary based on workload and capacity requirements.
Our capacity utilization at the device level is higher (we’re achieving better than 82% in customer production environments today and expect to increase that to the mid to high 80s by the end of this year). Our AFAs today have the highest TB/watt metrics,¹ and we guarantee that our systems are the most energy efficient in the industry.
The Necessity of Modern Storage for AI-RAN Modern RAN architectures must evolve to address the increased data traffic, diverse service requirements, and the necessity for rapid scalability and deployment. The challenge lies in extracting data from network functions in real time and managing this data holistically.
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