# Nvidia wants to bypass the CPU with an open-source AI storage overhaul

> Source: <https://www.sdxcentral.com/news/nvidia-wants-to-bypass-the-cpu-with-an-open-source-ai-storage-overhaul/>
> Published: 2026-08-06 15:23:10+00:00

Nvidia is looking to help speed up AI workloads by open-sourcing an API that lets a graphic processing unit (GPU) read and write directly to storage without having to source data from the central processing unit (CPU).

The [cuFile](https://github.com/xio-sig) API was published to GitHub, with Google, Intel, and Meta joining the chip giant as core maintainers. Its open-sourcing comes amid a renewed focus on the CPU given its importance in agentic AI stacks as the central control plane and orchestration engine. CuFile, however, enables the movement of data through a drive to GPU memory without the need for a bounce buffer from the host CPU’s random access memory (RAM)

CuFile essentially allows hundreds-of-thousands of GPU threads to securely access data from storage “in just microseconds.”

Nvidia also open-sourced the scaled accelerated data access (SCADA) vertical storage software stack that sits beneath the API. The chip giant said the move would “help make security context, data and storage accessible at the speed AI-powered defenses need.”

The SCADA framework allows parallel GPUs to extract specific data required for applications directly into high-speed memory, optimizing performance by bypassing unnecessary information.

[DDN](https://www.ddn.com/press-releases/ddn-and-nvidia-collaborate-to-advance-gpu-initiated-data-access-for-next-generation-ai/) is an early adopter of the platform and is using it to provide more efficient connections between Nvidia GPUs and the vendor’s Infinia data intelligence platform.

“AI success will increasingly be measured not by how much infrastructure an organization owns, but by how productively it uses that infrastructure," DDN CTO Sven Oehme explained. “Our work with Nvidia is focused on helping customers keep their GPUs working, accelerate time to insight, and improve the economics of AI at scale. By reducing the friction between compute and data, we can help organizations generate greater value from every accelerator, watt, and dollar invested.”