What’s Next for AI Leaders: Manage Data, Not Infrastructure

Pure Accelerate 2026 was a big win for Everpure. We had the pleasure of hosting over 1,000 customers and partners at the amazing Resorts World Las Vegas. The event featured a week full of robust conversations about how to manage data so AI factories can move from experimentation to production with less friction and faster return on infrastructure investment. The strongest engagement came when discussions shifted from component specs to keeping GPUs utilized as AI demand rises.

Recently, the Everpure performance lab was able to push our SPEC AI_Image record to 7,200 simultaneous AI jobs. With our recent support for multi-tenancy, QoS, and security enhancements, FlashBlade//EXA™ is now more than ever uniquely positioned as the highest throughput unified AI storage solution at scale. 

As compelling as those lab results are, STN’s session at Pure Accelerate stole the show. The managed infrastructure company was able to achieve some truly remarkable and scalable performance metrics for real GPU as-a-service workloads. Let’s take a closer look.

Sabur Mian, STN’s founder and CEO, described FlashBlade//EXA as the storage foundation for enterprise-grade GPU as a service. The value of its disaggregated architecture, strong throughput, and serviceability in real AI environments make FlashBlade//EXA a standout solution in the AI storage ecosystem. STN was able to reach 95–96GB/s per node in testing while simplifying storage management for complex GPU clusters. 

The unique performance profile of FlashBlade//EXA allows businesses to save money on local NVMe drives for compute clusters. That is exactly the kind of production behavior AI leaders are looking for as they turn AI infrastructure into repeatable services.

Following the keynote presentations and breakout sessions, we invited a select group of VIP clients to attend “Scale AI Day” at Speed Vegas. This bonus event gave us a chance to share ideas in a more informal setting. Everpure CEO Charles Giancarlo and CTO Robert Lee provided exclusive insight into our AI technology roadmap for 2027. Special guest Jason Hardy of NVIDIA shared what is next from the GPU leader. 

From the Everpure booth to the Vegas Strip, FlashBlade//S™ users were asking how and when to upgrade to FlashBlade//EXA. The more useful question is this: When should AI service consumers and providers choose FlashBlade//EXA?

AI leaders and cloud service providers are dealing with three practical problems at once. First, they’re trying to turn next-generation pilots into monetizable services. Second, they’re trying to stop infrastructure bottlenecks from eroding GPU utilization. Third, they’re trying to scale without creating another layer of operational sprawl and cost. These are precisely the conditions FlashBlade//EXA was built for.

AI pilots stall when the data path is not ready for production

AI projects do not fail because organizations lack ambition. They fail because the full path from data to outcome is not production-ready. If the underlying data platform is fragmented or slow to feed the infrastructure, the project never scales cleanly. That is why the right framing is to manage data, not infrastructure.

Everpure helps solve that with FlashBlade//EXA, a unified AI data platform for training, inference, and large-scale AI workflows. It brings together high-performance file access, S3-compatible object support, and a disaggregated architecture that separates metadata from bulk data. That means organizations can simplify their operating model while reducing storage silos and create a cleaner foundation for AI services that need to move beyond the lab.

GPU investments lose value when metadata and throughput become bottlenecks

Underutilized GPUs, inconsistent performance, metadata bottlenecks, and complex siloed environments are limiting ROI. That is the core issue AI leaders need to focus on next. Simply adding compute resources does not necessarily lead to improved access to data. If storage cannot sustain parallel reads, metadata responsiveness, and predictable throughput, the factory slows down long before the GPUs reach their potential.

Everpure™ FlashBlade//EXA removes that constraint. The architecture uses FlashBlade-based metadata services with scale-out NVMe data nodes. That separation keeps metadata operations responsive while allowing the data tier to scale for bandwidth and capacity. The result is a platform designed to keep GPUs fed instead of forcing operators to spend more time compensating for the storage layer. In a market where throughput per GPU increasingly defines business value, that is not a feature. It’s the foundation.

Storage redesigns should not be required to scale AI

The third problem is both economic and operational. AI leaders know demand is moving quickly. AI factories and hyperscale environments all require very different ranges of throughput, capacity, and GPU count. What starts as a manageable deployment can quickly become a platform problem if the architecture forces linear growth, rigid scaling, or another replatforming event just as the business gains momentum.

FlashBlade//EXA lets neoclouds and AI-native providers scale metadata and data independently, alleviating bottlenecks and cache collapse. FlashBlade//EXA provides premium metadata performance where concurrency matters most and a common Ethernet-based architecture that is simple to integrate into modern AI environments.

What leaders should take away 

The organizations that win the next phase of AI must pivot away from managing their infrastructure to managing their data. That requires making the correct technology investments at the correct time. The Everpure Platform features FlashBlade//EXA, the benchmark in scalable, unified storage. With FlashBlade//EXA, neoclouds can create solutions and services on a production-ready data platform that scales without complexity. Do not waste cycles with AI storage that bottlenecks when you need to scale. Trust Everpure FlashBlade//EXA, the fastest and most scalable unified storage system for AI. See you at Pure Accelerate 2027.