Solutions
AI storage demand is not uniform. Training reads need throughput, checkpoints need durable low-latency writes, and most of the corpus is untouched between runs. AiRE lets each of those be a different data set on the same system.
The problem
A pipeline that starts as an experiment becomes an estate. Raw data lands, gets cleaned, gets versioned, and every intermediate stage is kept because reproducing it is expensive. Within a year the corpus is an order of magnitude larger than the data anyone is actively reading.
The conventional answer is two systems: fast storage for the working set, an archive for the rest, and a migration process joining them. That process is where the operational cost hides — someone has to decide what moves, when, and what breaks if it moves at the wrong moment.
The alternative usually offered is to put everything on all-flash and stop worrying. That works, and it is how storage budgets get consumed by data nobody is reading.
What STORViX does about it
Because AiRE applies profiles, encryption and reduction per data set rather than per array, the fast path and the cold corpus can live in the same unit under one interface.
The AAF disk pack uses a flash tier to manage I/O path metadata, which is the configuration STORViX positions for mission-critical and intensive workloads. Accelerated Hybrid Flash sits below it where the blend matters more than raw speed.
Assigning the Performance profile prioritises lower latency and higher throughput, with policies adjusting I/O path management, cache behaviour and block size to suit. Variable block sizes matter when a pipeline mixes many small files with large sequential reads.
The same unit runs the archive under a profile that minimises system load and maximises usable space. In-line deduplication with 256-bit checksums and compression via LZ4 or GZIP9 apply during the write, not as a later pass.
Copy-on-write snapshots produce clones instantly and with no additional space. Branching a dataset for a new experiment stops being a copy operation with a capacity cost.
Every block is checksummed to the root node, and a mismatch on read triggers repair from a redundant copy. A corrupted checkpoint that reads back as valid is a failure mode that costs a training run.
CloudSight analyses utilisation and workload patterns to project future capacity, which is what makes FlexiPay's consumption model workable rather than a guess.
AiRE platform
AiRE is a proprietary software stack on a storage-optimised UNIX operating system, built around a 128-bit pooled file system with no physical limit on expansion. An unlimited number of data sets can exist across pools in the same unit, each carrying hundreds of parameters of its own.
At a glance
From the STORViX Technology Guide and Our Hardware product sheets.
What AiRE is
AiRE is STORViX's software stack, built on a storage-optimised UNIX operating system with a 128-bit file system and software-defined data services. It is integrated with physical or virtual hardware to get the most out of it; either deployment is called an AiRE instance.
One-way telemetry feeds CloudSight, where machine learning identifies emerging issues. A backlink from the cloud makes centralised fleet management possible, and AutoPILOT acts on most of it without waiting for a human — what the product sheets call a self-driving storage array.
There is a great deal of unfalsifiable performance marketing in AI infrastructure. This is what STORViX can and cannot support with published material.
STORViX has been in the NVIDIA Inception programme since 2022 and works with Oracle and AWS. Those are relationships, not benchmarks.
No throughput, IOPS or GPU-utilisation figure appears on this page, because none is published that would survive contact with your pipeline. Storage performance for AI work depends on file size distribution, access pattern, network fabric and the framework's own I/O behaviour — which is why the useful number comes from a proof of concept against your data.
What is published and verifiable: the disk pack tiers, the profile and policy model, the reduction algorithms, the integrity mechanism, and the capacity envelope. Those are the grounds on which to shortlist. The performance conversation should happen with an architect and your own dataset.
A 30-day evaluation licence runs the full feature set up to 32 TiB. Bring a representative slice of your corpus and the access pattern that actually matters.