THE AI
STORAGE
FRONTIER.
Beyond the SAN. Beyond the array. Explore the architectures feeding the world's most demanding AI systems—from open-source distributed clusters to disaggregated, GPU-scale data fabrics.
Explore the data path.
Select an architecture to see how compute, metadata, network and persistent media work together. The key distinction is what scales together—and what can scale independently.
Diagrammatic simplifications. Not physical wiring diagrams.
Traditional enterprise SAN
Tightly integrated array architecture designed for predictable enterprise block I/O.
A faster transport does not remove software, controller or metadata bottlenecks. Design from workload behaviour, not from headline IOPS.
Same flash. Different paths.
Mature enterprise block I/O. Dedicated fabric options (FC) or standard Ethernet networking (iSCSI).
NVMe commands over conventional Ethernet/TCP, without the requirement for an RDMA fabric.
Low-overhead transport over an RDMA-capable fabric; requires compatible NICs and tuned networking.
An I/O path optimisation that reduces redundant CPU-memory copies when hardware and software support it.
Different models.
Different bottlenecks.
Switch AI workloads to see their conceptual I/O emphasis and which storage families merit investigation. Fit labels are editorial starting points, not measured rankings.
Where to look first.
Real procurement requires the same workload, network, client count and failure conditions on every candidate platform.
Not a leaderboard.
A set of trade-offs.
Compare design patterns and operational consequences instead of fabricating vendor IOPS scores across incompatible deployments.
Selection depends on proof-of-concept evidence.
| Platform | Core design | Scaling model | Best discussion | Verify before buying |
|---|
⚠ Read this before comparing: OpenStack orchestrates infrastructure; NVMe/TCP and NVMe/RDMA are transports; Ceph and the products listed are storage/data platforms. They are different layers of the stack, not interchangeable competitors.
What does sovereign AI look like at hundreds of petabytes?
PUBLICLY ANNOUNCED PARTNERSHIPS2023 · G42 Cloud + VAST Data. The partners announced a multi-hundred-petabyte AI data cloud using VAST's DASE architecture, with multi-tenant data services intended to support large-scale AI supercomputing.
2026 · Core42 + e& UAE. The partners separately announced an in-country sovereign AI compute offering. These statements show the design direction, not the detailed topology of Core42's current customer installations.
Published data.
Real constraints.
MLPerf Storage v3.0 (September 2026) provides workload-level results—more meaningful than comparing isolated device specifications. Here is a measured checkpoint example.
1.25T parameter checkpoint writes
Published MLPerf Storage v3.0 vendor-submitted results. Write bandwidth in GiB/s; these are different-size deployments, not an equal-hardware head-to-head.
GPU busy time beats peak IOPS.
When accelerators stall waiting for data, storage can become a compute-cost problem. Validate where stalls arise before prescribing infrastructure.
Bars are illustrative visual emphasis, not measurements.
What does an idle
GPU fleet cost?
Explore the potential annual value of lost productive GPU time and how data-protection choices change raw-capacity economics.
Compute waiting-time exposure
This is a scenario calculator—not a vendor quote, measured idle-time percentage, or guaranteed storage saving.
256 × $4/hr × 8,760 hours × 10%
Assumes all GPUs are provisioned for 8,760 hours per year. This measures opportunity cost, not cash savings. Do not attribute all idle time to storage without telemetry.
What 1 PB of data really consumes.
Theoretical raw-capacity requirement for 1 PB of logical data under different protection ratios, before filesystem overhead, snapshots, reserve capacity or data reduction.
Lower raw-capacity demand can come with CPU, rebuild, write-amplification and latency trade-offs. Protection geometry must respect real failure domains.
Formula: raw capacity = logical data × (data chunks + parity chunks) / data chunks. Replication factor 3 = logical data × 3.
Before you write
the RFP.
For a sovereign AI cloud, the selection is as much about operations and jurisdiction as theoretical flash bandwidth.
Profile actual I/O
Read/write sizes, concurrency, P99 tail latency, metadata operations, checkpoint intervals and real GPU stall traces.
Map tenants & keys
Namespace boundaries, authentication, customer-managed keys, cross-region replication rules and data residency.
Break it on purpose
Node outages, degraded performance, metadata failover, erasure rebuild, immutable recovery and tested RPO/RTO.
Validate the fabric
NVMe/TCP vs RDMA, congestion control, RoCE design, host CPU cost, GPU Direct paths and network oversubscription.
Compare full cost
Useful bandwidth per dollar and watt, licensing, support, platform refresh, rack density and engineering headcount.
Run realistic pilots
Same workload, same clients, same failure cases. Record sustained throughput and P95/P99—not peak marketing figures.