In the News

This Electronic Design webinar with William G. Wong, J Michel Metz Ph.D, Jason Molgaard and Scott Shadley introduces Storage.AI, a SNIA project to optimize large scale storage architectures for GPUs and AI accelerators. Supporting artificial intelligence's (AI) insatiable need for compute and storage is why SNIA put together the Storage.AI project.  

In this episode of the Scaling Intelligence podcast, Intersect360 Research's Addison Snell sits down with SNIA board chair Dr. J Metz and communications co-chair Scott Shadley to introduce the SNIA Storage.AI project and explore the evolving landscape of data storage and infrastructure.

Digitalisation World's Phlip Alsop, interviews Dr. J Metz, SNIA Chair, who explains how AI workloads are extraordinarily complex and constrained by issues related to latency, space, power and cooling, memory, and cost and how addressing these problems through an open industry initiative is the fastest path to optimization and adoption, before going on to introduce Storage.AI, an open standards project for efficient data services related to AI workloads. Storage.AI will focus on industry-standard, non-proprietary, and neutral approaches to solving AI-related data problems to optimise the performance, efficiency, and cost-effectiveness of AI workloads.

In this podcast episode, Chris Evans discusses Storage.AI, a new initiative from SNIA with Chair, Dr. J Metz. Dr. J works for AMD as a Technical Director but is also on several industry committees, acting as Chair for SNIA and the Ultra Ethernet Consortium.

The adoption of AI raises many issues for data processing and data management, resulting in potential inefficiencies across infrastructure. Storage.AI is an initiative aimed at bringing the storage industry together to use existing and new standards to make the processing of data for AI efficient and as fast as possible.

SNIA is working on an open standards project for efficient data services related to AI workloads, focusing on industry-standard, non-proprietary, and neutral approaches.The Storage.AI project will work to build broad ecosystem support with SNIA’s partners, including UEC, NVM Express, OCP, OFA, DMTF, and SPEC. 

In the modern AI era, networks are being stressed in new ways that are putting strain on multiple components of infrastructure. Storage.AI focuses on data-handling efficiency after packets reach their destinations. Founding members include AMD, Cisco, Dell, IBM, Intel, NetApp, and Pure Storage.

SNIA’s new Storage.AI initiative, backed by tech giants, promises to clear the roadblocks that cripple modern AI performance. Storage.AI is an ambitious open-standards initiative backed by 15 industry titans—from AMD to Intel to IBM—to tackle one of AI’s most overlooked bottlenecks: data access after it hits the network.

SNIA is releasing a Storage.AI project that looks to reframe the AI storage discussion. Storage is becoming a bigger topic in AI clusters since keeping GPUs fed and working can have a multiple-billion-dollar ROI.

Datadobi CTO, Carl D'Halluin, will be a featured speaker at the 2025 SNIA Developer Conference (SDC'25), speaking on "War Stories from the Storage Trenches: Moving Data Across NFS, SMB, and S3."

The ever-growing demands of AI and HPC workloads mean that there’s a sense of urgency to solve the performance bottlenecks with current memory designs.

The SNIA’s DNA Storage Alliance has published a 52-page technology review looking at the data encode/decode tech, commercial readiness metrics, and the challenges ahead.

DNA Data Storage Technology Review, and DNA Data Storage Codecs - Examples, Requirements, and Metrics

Deoxyribonucleic acid (DNA) computing and data storage are emerging fields that are unlocking new possibilities in information technology. Here, we discuss technologies and challenges regarding using DNA molecules as computing substrates and data storage media.

Designed for hyperscale data centers, a revolutionary new SSD form factor could soon redefine high-density storage in data centers by narrowing the capacity gap between mechanical hard drives and solid-state drives.  

Once reserved primarily for archival use, large-scale data lakes are now asked to support increasingly active workloads. New AI applications, analytics pipelines, and emerging digital services are driving up the “temperature” of previously considered cold data. The result is a growing middle ground, where data is accessed too frequently for HDDs to handle efficiently, but not hot enough to warrant the cost of performance SSDs. E2 is a new flash form factor to address this emerging “warm” storage tier. It is designed to bridge the gap between high-capacity hard drives and traditional enterprise SSDs, offering a more practical balance of performance, density, and cost.

SNIA assembled three CXL experts who provided an overview of the considerable progress in developing the CXL standard. From the performance data, it’s now possible to determine which applications are best suited for using CXL-based memory subsystems and which are not.