StorageAI™ | SNIA | Experts on Data

Solving AI Data Challenges:

StorageAI is an open standards project for efficient data services related to AI workloads. We are addressing the most difficult challenges related to AI workloads, closing existing gaps in processing and accessing data.

AI exposed urgent problems in data services, especially around how storage interacts with compute.
Data pipelines are inefficient— round-tripping between storage and compute is wasting power and performance.
Accelerators (like GPUs) go idle when data isn’t available at the right place and time—and that’s incredibly expensive
StorageAI focuses on vendor-neutral, interoperable solutions for issues like memory tiering, data movement, storage efficiency, and system-level latency that take advantage of GPUs and AI acceleration.

Ongoing SNIA Technical Work

Enables

SDXI (Smart Data Acceleration Interface)

Vendor-neutral DMA acceleration across CPU, GPU, DPU; memory-to-memory copies and transforms

Computational Storage API & Architecture

Allows SSDs and storage devices to perform computation (e.g. filtering, inferencing, format conversion) directly

NVM Programming Model

Unified software interface for accessing NVMe, SCM, and RDMA memory/storage tiers

Swordfish / Redfish Extensions

Unified, Redfish-compatible storage management for hybrid and disaggregated infrastructure

Object Drive Workgroup

Standard interfaces for object storage devices to operate in RDMA / hyperscale environments

Flexible Data Placement APIs

Works with computational storage and GPU-aware storage pipelines to optimize layout and streaming throughput

Future Work Planned

File/Object over RDMA and Ultra Ethernet

Enables hybrid file/object storage backends, allowing traditional applications to benefit from S3-like scalability, combined with the speed and efficiency of RDMA

Accelerator Direct Access Bypass

Bypasses CPU for data movement between GPU memory and RDMA/NVMe, enabling near-zero-latency I/O to accelerators

Accelerator-Initiated I/O

GPUs can initiate and complete their own I/O operations (e.g. checkpoint writes, data loading), reducing CPU bottlenecks. Also known as Accelerator-Initiated Storage I/O (AiSIO).

"This isn’t just a new protocol. It’s a fundamental reimagining of how AI data moves."