Digital Event Horizon
The Evolution of AI Storage: A New Era of Accelerated Computing and Secure Data Access
AI systems require massive amounts of data to function effectively, but creating an efficient and secure storage infrastructure is not enough; it's about how these applications access that data. At the Future of Memory and Storage (FMS) conference, NVIDIA unveiled new storage advancements that showcase how accelerated computing enables AI applications to access storage directly – fast enough to act like memory and secure by design. Learn more about how this new era of AI storage is changing the game for AI systems and their developers.
AI systems require massive amounts of data to function effectively, but inefficient storage infrastructure can become bottlenecks.NVIDIA's Vera CPU delivers up to 3.21x higher throughput than an x86 CPU in a two-stage compression and encryption pipeline.Accelerated computing enables AI applications to access storage directly, making it faster like memory and more secure by design.The tradeoff between data access speed and storage space is now measured in microseconds, not minutes.NVIDIA is open sourcing its cuFile APIs and vertical storage software stack to enable interoperability across the ecosystem.cuFile enables securely accessing data from storage in just microseconds, providing fast, secure access to data and storage.The Open Secure AI Alliance and Storage-Next initiatives aim to advance the field of AI storage and make security context, data, and storage accessible at scale.NVIDIA's SCADA framework allows massively parallel GPUs to pull only the necessary data directly from storage into their own high-speed memory.
AI has been rapidly advancing over the past few years, but one key factor that has been driving this progress is the increasing demand for memory and storage. As AI systems become more complex and sophisticated, they require massive amounts of data to function effectively. However, simply adding more storage capacity is not enough; it's about creating an efficient and secure storage infrastructure that can keep up with the demands of these AI applications.
At the Future of Memory and Storage (FMS) conference, NVIDIA unveiled new storage advancements that showcase how accelerated computing enables AI applications to access storage directly – fast enough to act like memory and secure by design. The pressure on this infrastructure is intensifying as AI agents consume massive amounts of data, and GPUs can now initiate storage requests directly, generating thousands of concurrent operations.
To serve these requests, storage systems must continuously encrypt, compress, verify, and reconstruct data. These critical data services can become bottlenecks when thousands of agents access storage simultaneously. Benchmarks highlighted in this NVIDIA technical blog show that the NVIDIA Vera CPU, part of NVIDIA Vera BlueField-4 STX, delivers up to 3.21x higher throughput than an x86 CPU in a two-stage compression and encryption pipeline.
This means that with Vera, storage platforms can absorb the flood of AI data more efficiently – delivering greater throughput with significantly less compute infrastructure. With accelerated computing, storage stops being a passive place to keep data and becomes an active part of the data path. This upends the old economics of determining when data belongs in memory (where applications can fetch it faster) versus on a storage drive (where it can be held in cheap and plentiful space).
The tradeoff was first framed 40 years ago, when the answer was measured in accessing that data in minutes. On today's GPUs, paired with AI storage solutions from NVIDIA and partners, the same tradeoff now plays out in microseconds.
Closing the gap between AI's needs and memory shortage depends on extreme codesign across the whole ecosystem, from memory and storage manufacturers to the software built on them. Open Source NVIDIA cuFile APIs Enable Interoperability for Storage Solutions at FMS, NVIDIA announced it is open sourcing its cuFile application programming interfaces (APIs) — and the vertical storage software stack underneath them.
This lets GPUs, not just CPUs, read from and write to storage directly. cuFile is an open source component of NVIDIA GPUDirect Storage. Using hundreds of thousands of GPU threads, fast high-bandwidth memory and other methodologies, cuFile enables securely accessing data from storage in just microseconds.
This represents how the industry is unifying a security-first storage stack based on Linux best practices, providing interoperability between GPUs and data. In addition, fast, secure access to data and storage is a foundational element to powering preventive and detective cybersecurity measures.
Making cuFile openly available will help make security context, data and storage accessible at the speed AI-powered defenses need. Such open technologies support initiatives such as the new Open Secure AI Alliance.
NVIDIA and Industry Leaders Advance New Frontier of AI Storage
In addition, NVIDIA and storage industry leaders are optimizing memory and storage solutions through an initiative called Storage-Next. The NVIDIA-driven initiative brings together storage makers, controller vendors, thermal design, cooling and orchestration operators, and standards bodies to align on how GPU-driven storage should behave — then turn these advancements into interoperable, open industry standards.
Storage-Next includes over 40 leading storage and flash vendors — including DDN, KIOXIA, and Micron — each contributing to the next generation of AI storage technologies with NVIDIA. The initiative is grounded in accelerated data access for large AI datasets.
To support this, NVIDIA offers SCADA — short for scaled, accelerated data access — a framework that lets massively parallel GPUs pull only the data necessary for the application directly from storage into their own high-speed memory.
For example, DDN is integrating SCADA with Infinia, its software-defined, AI-native data intelligence platform built to eliminate storage bottlenecks at scale. “AI success will be defined not by how much infrastructure organizations own, but by how productively they use it,” said Sven Oehme, chief technology officer at DDN.
“Our collaboration with NVIDIA is helping create a more direct, efficient connection between GPUs and data — keeping accelerated computing resources productive, speeding time to insight and enabling customers to achieve stronger business and financial returns from their AI investments.”
Storage-Next and SCADA extend NVIDIA’s longstanding work on AI storage infrastructure, including on NVIDIA Vera BlueField-4 STX — a modular, rack-scale foundation powered by the NVIDIA Vera Rubin platform, NVIDIA Vera BlueField-4 storage processors, and NVIDIA Spectrum-X Ethernet networking.
NVIDIA Vera BlueField-4 STX storage processor defines a new class of AI-native data platforms. It uses the unified NVIDIA DOCA security stack to let enterprises enable continuous policy enforcement in the AI data path. Plus, NVIDIA CMX Context Memory Storage provides an AI-native context tier for long-context, multi-turn, agentic AI inference, built on NVIDIA STX.
Speed at the storage layer comes with a catch. Letting an application talk straight to a drive is quick, but done carelessly, it can scribble over other processes' memory — a security hole, not a feature. NVIDIA SCADA uses a safe, robust method to achieve scaled direct access by splitting the job in two:
The user parts of an application that need raw speed stay outside the trusted computing base. A separate, privileged component configures protected access between the user application and its approved storage at setup, adhering to standard Linux protocols for security enforcement while efficiently safeguarding data.
It’s all part of how advancements in fast, massively parallel, efficient, secure AI storage infrastructure can feed better data to applications and AI factories — so they can produce more useful, accurate, grounded intelligence at scale.
Join NVIDIA sessions at FMS, running August 4-6 in Santa Clara, California, and learn more about NVIDIA AI storage. See notice regarding software product information. NVIDIA GTC Berlin Registration Is Now Open October 20-22 Register Now Recent News AI Firebird Launches CIS Region’s Largest AI Factory in Armenia August 8, 2026 Gaming GeForce NOW Shakes Up August With 26 New Games August 6, 2026 AI Into the Omniverse: How Open World Models Push the Frontier of Physical AI August 6, 2026 AI NVIDIA and Partners Build in America, for America August 5, 2026 View All Recent News Categories AI Infrastructure Tags Agentic AI AI Factory Artificial Intelligence Cybersecurity Events Hardware NVIDIA BlueField NVIDIA Rubin NVIDIA Spectrum-X Ethernet NVIDIA Vera Open Source Related News AI Firebird Launches CIS Region’s Largest AI Factory in Armenia Aug 8, 2026 AI Into the Omniverse: How Open World Models Push the Frontier of Physical AI Aug 6, 2026 AI Infrastructure NVIDIA Joins NSF State and Regional AI Hubs Program to Expand AI Research and Education Across the US Aug 4, 2026 AI AI Leaders Propose SAFE Guidelines for Cybersecurity Transparency Aug 4, 2026 Share This Facebook LinkedIn Share on Mastodon Enter your Mastodon instance URL (optional) Share
Related Information:
https://www.digitaleventhorizon.com/articles/The-Evolution-of-AI-Storage-A-New-Era-of-Accelerated-Computing-and-Secure-Data-Access-deh.shtml
https://blogs.nvidia.com/blog/ai-storage-fms/
Published: Mon Aug 10 18:33:04 2026 by llama3.2 3B Q4_K_M
