Nutanix announced that its Nutanix Unified Storage (NUS) solution is now NVIDIA-Certified at the enterprise level, validating the platform for enterprise and cloud provider deployments running large-scale production AI workloads. The certification provides a validated configuration intended to reduce integration risk and help customers scale AI infrastructure with more predictable storage behavior.

The company also disclosed plans to extend its AI-native storage roadmap with support for NVIDIA Vera BlueField-4 STX. Nutanix framed this as an effort to improve data access and storage efficiency while simplifying operations as AI environments grow.

Addressing Storage as a GPU Utilization Constraint

As organizations build out “AI factory”-style infrastructure, Nutanix is targeting a recurring problem in production environments: GPU capacity is often limited by the ability to consistently feed data to accelerators. Fragmented infrastructure, siloed datasets, and inconsistent I/O can introduce bottlenecks that slow deployments and reduce effective GPU utilization. Nutanix is positioning NUS, along with its NVIDIA certification, to deliver a more consistent data path and reduce deployment variability across the stack.

Thomas Cornely, EVP of Product Management at Nutanix, emphasized that the goal is to remove infrastructure fragmentation and data silos so AI pipelines can sustain reliable throughput at scale. Jason Hardy, VP of Storage Technology at NVIDIA, similarly highlighted storage as a gating factor for enterprise AI, noting that certification provides customers with a more interoperable platform, reducing bottlenecks and improving GPU efficiency. Both sets of comments centered on interoperability, predictable scaling, and validated configurations rather than point performance claims.

Reference Architecture

Nutanix described the NVIDIA-Certified NUS reference architecture as being built on a 10-node all-NVMe cluster. On the protocol side, it uses enhanced parallel NFS (pNFS) and GPUDirect Storage over NFS with RDMA. The objective is a low-latency, high-throughput data path between GPU hosts and storage, designed to maintain resilience and minimize downtime as environments scale.

For the network fabric, Nutanix stated the design uses NVIDIA Spectrum-X Ethernet, including Spectrum-4 switches and BlueField-3 DPUs. Nutanix also provided scaling figures, claiming linear performance growth from 10GB/s read and 5GB/s write at 32 GPUs up to 160GB/s read and 80GB/s write at 1,024 GPUs.

Workload Coverage and Supported GPU Platforms

Nutanix positioned the architecture as a foundation for a range of AI workflows, including training, fine-tuning, inference, and RAG pipelines. The company also described broad compute compatibility, citing support for x86-based systems and multiple NVIDIA GPU configurations, including NVIDIA RTX 6000 PRO Blackwell, NVIDIA H200 NVL, NVIDIA HGX platforms with B200, H200, or H100 GPUs, and NVIDIA GH200 Grace Hopper Superchip configurations.

Availability

Nutanix said the NVIDIA-Certified Nutanix Unified Storage reference architecture is available now. Planned support for NVIDIA BlueField-4 STX is expected in the second half of 2026.

The post Nutanix Unified Storage Earns Enterprise-Level NVIDIA Certification for Production AI Workloads appeared first on StorageReview.com.

The Storage Review team visited the Dell campus in Hopkinton to go hands-on with the latest advancements in enterprise storage. This podcast takes place in one of our favorite places, the Hopkinton Dell Lab. Brian connects with Scott Delandy, an Engineering Technologist at Dell, for this interaction to go in-depth on the PowerStore Elite. This discussion covers the transition from PowerStore Gen 2 to the new PowerStore Elite, exploring the hardware architectural shifts and the software-driven intelligence defined by PowerStore OS 5.0.

While in the Hopkinton lab, we filmed a deep dive with Dell’s Technical Marketing Engineer, Stephen Granger, where Kevin and Steve tear down the 9500 controller and compare it to the 1500 controller. It gets technical with details around PCIe lanes, drive connectivity, battery backup architecture, serviceability, and new IO module form factor. It’s worth a look as a companion to the podcast video.

This short podcast and the Hardware Deep Dive videos literally tear down the PowerStore Gen Elite to give you an in-depth view of the internals. Brian and the team cover the upgrades from Gen 2 to Gen 3, providing the information you need to understand how the PowerStore Elite fits your environment. If you don’t have 30 minutes to spend, we’ve broken the segments down into five-minute increments so you can hop around to the sections that apply to your environment and interests.

[00:00-05:00] Introduction

The conversation opens with a transition from lab testing to the platform’s official rebranding and the key components of the new release.

• Official rebranding of PowerStore Gen 3 to PowerStore Elite.
• Integration of PowerStore OS 5.0 with the new Gen 3 hardware platform.
• Backward compatibility of OS 5.0 features for existing PowerStore installations.
• Hardware architecture leap featuring PCIe Gen 5 and DDR5 memory.
• Shift to E3.S and E3.L flash media modules for high-density storage.
• Standardization on OCP IO modules to streamline the component ecosystem.

[05:00-10:00] Hardware Standardization and Strategy

The dialogue focuses on the strategic decision to leverage common components across the Dell ISG portfolio and the benefits for supply chain resiliency.

• Supply chain advantages gained by using standardized PowerEdge server components.
• Reduced lead times for systems through a consolidated parts bucket strategy.
• Distinction between off-the-shelf server hardware and purpose-engineered storage controllers.
• Internal bus and memory access optimizations specifically for storage workloads.
• The interaction between hardware and software in autonomous operations drives significant performance gains.

[10:00-15:00] Cybersecurity and Ransomware Resiliency

As security becomes a primary objective for storage administrators, the discussion highlights the platform’s new detection and recovery capabilities.

• Enhancing cyber resiliency within primary storage rather than relying solely on secondary backup.
• Native ransomware detection using anomaly patterns and signature monitoring.
• Reducing Recovery Point Objectives (RPO) and Recovery Time Objectives (RTO) through faster detection.
• Granular telemetry monitoring at the individual IO level for application security.
• Maintaining peak storage performance while running background analysis for security threats.

[15:00-20:00] AIOps and the Shift Toward Automation

The speakers explore the cultural shift in storage administration from Manual “knob-turning” to trusting machine-driven automation and intelligence.

• Evolution of storage administrator roles from specialists to infrastructure generalists.
• Automating routine tasks like software updates and security patches to reduce admin effort.
• Building trust in infrastructure to perform prescriptive remediations automatically.
• The impact of cloud-native experiences on user expectations for on-premises management.
• Future roadmap for AIOps to provide increasingly actionable intelligence and fewer manual approvals.

[20:00-2500] Enterprise Data Services and Legacy Integration

The segment looks back at the foundational technologies of the past 32-bit and 64-bit file systems while defining modern enterprise requirements.

• The architectural heritage of modern file code is rooted in legacy platforms like VNXe.
• Challenging the “midrange” label by focusing on enterprise-grade resiliency and data services.
• Advanced replication topologies and active-active failover capabilities.
• Integration with modern orchestration layers, including Kubernetes through CSI and CSM.
• Empowering failover and failback operations at the orchestrated level without manual storage intervention.

[25:00-30:00] Segment 6: Infrastructure Flexibility and Modern Ecosystems

A discussion on how storage must now fit into a complex ecosystem of diverse hypervisors, containers, and multi-cloud environments.

• Shifting from evaluating storage in isolation to assessing its fit within the entire infrastructure stack.
• Simplifying deployment through pre-built playbooks for Ansible and Terraform.
• Managing the complexity of modern environments involving Nutanix, Azure, and Kubernetes.
• Prioritizing product roadmaps based on user pain points and market telemetry.
• The move toward a faster software release cadence is to stay aligned with evolving tech trends.

[30:00-35:00] Maintenance, Migration, and Life Cycle Management

The podcast concludes with a focus on ease of serviceability and the simplified path for migrating data to the latest generation of hardware.

• Serviceability improvements, including intuitive latches and accessible boot drives to minimize downtime.
• Zero-impact migration paths through inter-cluster migration tools.
• Preserving path management at the cluster level during hardware refreshes.
• Economic strategies for utilizing older gear as Tier 2 or Tier 3 storage.
• Future-proofing the architecture to support subsequent controller upgrades and technology generations.

The post Podcast #149: Dell PowerStore Elite: Hands-on Deep Dive From the Hopkinton Lab appeared first on StorageReview.com.

Storage refreshes usually come in two flavors. There’s the quiet uplift, where a vendor rolls in a new CPU, claims a few percentage points of performance, and ships the same chassis with a different sticker. And then there’s the generational reset, where the chassis, drives, interconnect, cache architecture, and management plane all move at once. Dell PowerStore Gen 3, branded at launch as PowerStore Elite, is the second kind, and it’s not even close. Every major subsystem in the platform has changed, and most of them have done so in ways that materially redefine what a unified array is supposed to look like in 2026.

We’ve been watching PowerStore since the beginning. In our view, this is the most consequential release since the original launch in 2020, and arguably the most aggressive storage platform reset any major vendor has shipped in years. Dell didn’t simply iterate on Gen 2. They rebuilt the platform from the chassis up, made bets on form factors and architectural choices that most of the industry hasn’t yet committed to, and engineered the result for a ten-year service life with multiple in-place controller upgrades. To explore these new models, Dell invited us out to Hopkinton, MA.

Storage density is a key selling point of the new Dell PowerStore, and Dell doesn’t disappoint on that front. The platform supports up to 40 E3.S NVMe drives in a 3U chassis, with planned support for E3.L drives, while keeping every bay user-addressable for data instead of reserving slots for cache SSDs. Dell has also significantly modernized the underlying hardware platform, moving to next-gen Intel processors, alongside DDR5 memory, end-to-end PCIe Gen5 connectivity, and OCP 3.0 modules that replace the older Dell-specific SLIC carrier design.

Connectivity between controllers scales up to 200GbE RDMA today, with a path to even faster speeds via a future I/O card upgrade. On the software side, PowerStoreOS 5.0 introduces new autonomous data path intelligence and log-structured metadata to optimize performance and endurance for high-capacity QLC flash, I/O-level telemetry to lay the groundwork for future inline ransomware detection, and dynamic resource sharing between block and file services. All PowerStore appliances are now unified out of the box, providing enhanced support for file- and block-scale-out and non-disruptive data mobility across clusters.

Dell has also added unaligned deduplication and enhanced compression offloads, a key factor behind the company’s decision to increase its data reduction guarantee from 5:1 in previous generations to 6:1 with the new platform.

The hardware changes and software updates don’t tell a complete story, though. The architectural decisions Dell made beneath them are material because they determine whether the platform will hold up over the next decade or look dated in a few years. The shift to E3.S/L drives, the larger chassis, the move to Software-Defined Persistent Memory, the cable-free midplane interconnect, and the decoupling of the inter-node fabric from CPU generation are all decisions that look forward rather than sideways. Taken together, they’re what make the Gen 3 chassis a credible foundation for the multi-generational upgrade story Dell is telling with Lifecycle Extension.

There are three new appliance models in the family. The PowerStore 1500 is the single-socket platform with 24 drive bays at launch and a 100 GbE RDMA inter-node fabric. The 5500 and 9500 are dual-socket on the same 3U chassis, with 40 drive bays and 200 GbE RDMA inter-node connectivity; the 9500 offers twice the memory and higher core counts than the 5500. A future controller-swap upgrade will enable the 1500 to scale to 40 drives and 200GbE RDMA. All three run the same PowerStoreOS 5.0 image, share the same OCP 3.0 I/O architecture, and support TLC or QLC media in the same model, with no performance penalty for switching to QLC. This eliminates the need to choose between separate model tiers for different use cases or performance requirements.

The Gen 2 platforms remain available, and existing PowerStore customers have a clear path forward with intelligent clustering. Gen 1, Gen 2, and Gen 3 appliances can coexist in a single cluster with non-disruptive workload mobility, which is the right answer for an install base that doesn’t want to shift platforms every few years.

Dell PowerStore Gen3 Specifications

All three Gen 3 models share a 3U dual-node chassis, the same OCP 3.0 I/O architecture, and a unified PowerStoreOS that supports scale-out block or file out of the box. They differ in CPU socket count, DRAM capacity, drive count, and inter-node bandwidth.

Specification	PowerStore 1500	PowerStore 5500	PowerStore 9500
Overview
Positioning	Mid	Mid / high-end	Flagship high-end
Chassis	3U, dual-node
Compute & Memory
CPU platform	Intel single-socket	Intel dual-socket	Intel dual-socket
CPU per appliance	2× 24-core @ 1.9 GHz	4× 24-core @ 1.8 GHz	4× 32-core @ 2.2 GHz
Memory per appliance	512 GB (16 × 32 GB)	1,024 GB (32 × 32 GB)	2,048 GB (64 × 32 GB)
Storage
Drives per base appliance	Up to 24 EDSFF	Up to 40 EDSFF	Up to 40 EDSFF
Drives per expansion (post-RTS)	44 EDSFF
Drive support	TLC: 3.84 / 7.68 / 15.36 TB · QLC: 30.72 TB
Min. drive config	TLC 6× 3.84 TB · QLC 7× 30.72 TB	TLC 6× 3.84 TB · QLC 11× 30.72 TB	TLC 6× 3.84 TB · QLC 11× 30.72 TB
Max raw capacity (base)	~737 TB (24 × 30.72 TB)	~1.2 PB (40 × 30.72 TB)	~1.2 PB (40 × 30.72 TB)
I/O & Networking
OCP 3.0 line cards per node	3 (+1 reserved)	5 (+1 reserved)	5 (+1 reserved)
Cross-node interconnect	100 GbE RDMA	200 GbE RDMA	200 GbE RDMA

Dell PowerStore Gen 2 vs. Gen 3

Every major hardware subsystem in Gen 3 series PowerStore appliances has advanced by at least one generation. The most consequential changes for capacity planning are the 3U chassis, the E3.S drive bays, and the jump from 2× 10 GbE to up to 200 GbE on the inter-node fabric.

The base 3U chassis has 40 drive slots on the 5500 and 9500. The 1500 ships with 24 populated bays, and an additional 16 bays unlocked via a future Data-In-Place upgrade. That works out to 13.3 drives per rack unit, up from 10.5 on Gen 2, or roughly 40% more drives per RU in the base appliance and 83% greater density on the post-RTS 44-drive expansion shelf. The drives themselves are standard E3.S 1T NVMe SSDs from multiple vendors, with no proprietary carrier, which reduces exposure to supply chain constraints or single-source dependencies. Capacities run 3.84 TB, 7.68 TB, and 15.36 TB in TLC, plus 30.72 TB in QLC, and the same set of options is supported across all three models (1500, 5500, and 9500) with no performance trade-off between media types.

The choice between TLC and QLC comes down to $/GB and density rather than performance tier. Minimum drive configurations vary slightly by model: TLC starts at 6× 3.84 TB across the lineup, while QLC starts at 7× 30.72 TB on the 1500 and 11× 30.72 TB on the 5500 and 9500. All drives are SED or FIPS-validated, and the platform supports +1 and +2 Data Resiliency Engine (DRE) configurations. Thermals improve as well, with Dell citing roughly 50% lower cooling requirement than an equivalent 2.5″ deployment, helped by the EDSFF airflow geometry. All 40 bays are also user-addressable for data, since the cache is now handled via Software-Defined Persistent Memory rather than by NVRAM drives that consume front bays, as on Gen 2.

	PowerStore Gen 2	PowerStore Elite (Gen 3)
Chassis & Platform
Base chassis	2U, 2-node	3U, 2-node
I/O Bus	PCIe Gen 3	PCIe Gen 5
Memory	DDR4	DDR5
Storage
Drive form factor	Up to 25× 2.5″ U.2 NVMe (dual-ported)	Up to 40× E3.S/L 1T NVMe (dual-ported)
Expansion shelf	Up to 24× 2.5″ U.2 NVMe	Up to 44× E3.S/L NVMe (post-RTS)
Cache strategy	U.2 NVRAM drives	Cache to local flash (SDPM)
I/O & Networking
I/O slots	3× SLIC / OCP 2.0 (PCIe Gen 3 x16)	Up to 5× OCP 3.0 (PCIe Gen 4/5 x16)
Cross-node connect	2× 10 GbE, RDMA	Up to 200 GbE, RDMA (400 GbE-ready)
Management & Power
Out-of-band mgmt (BMC)	EMC GEM	iDRAC (storage version)
Power	EMC BBUs / custom PSUs	PowerEdge BBU / PSUs

Inside the PowerStore Gen 3 Hardware Platform

Looking at the front of the new 3U PowerStore Gen 3 unit compared to the previous Gen 2, the updated design carries the same look and feel as the 17th-generation PowerEdge servers we have previously reviewed. It ships in Dell’s newer grey colorway with the matching honeycomb bezel that has become a visual signature across the company’s current enterprise hardware lineup.

Compute & Memory

On the compute side, Intel CPUs power the system, with per-CPU TDP ranging from 165W to 270W and up to 32 cores per CPU on the 9500. Dell quotes up to 50% more cores per node. Memory moves to DDR5 throughout, with the 9500 shipping with 2 TB per appliance (64 × 32 GB DIMMs), the 5500 with 1 TB, and the 1500 with 512 GB. Dell’s shift to DDR5 also significantly increases memory throughput, offering 2x higher bandwidth than previous DDR4 generations.

The internal fabric jumps to PCIe Gen 5, four times the per-lane bandwidth of the Gen 3 fabric used in Gen 2 PowerStore. Socket layout is the main differentiator across the lineup: the 1500 is single-socket, while the 5500 and 9500 are dual-socket, all running the same chassis and the same PowerStore OS software image, with core count and memory as the levers for right-sizing.

Chassis Cooling

Cooling on the 9500, pictured below, is an impressive design. The two CPU coolers on a single controller are connected via heat pipes and extended into a wide, conjoined fin stack, which is a smart move given how much heat a dual-socket controller has to dissipate. An earlier design choice comes into play for cooling: the 3U chassis. Each controller is now 1.5U tall, giving air a longer path to flow through than a 1U tray. This gives each model plenty of cooling headroom throughout the platform’s lifespan. The 1500 uses a more traditional-looking CPU cooler but retains all the airflow benefits of the 5500 and 9500 models. Regardless of the model, cooling across the family is handled by six fans per controller, giving the unit a total of 12 fans that keep the drives and the rest of the unit within their thermal envelope.

Networking and Storage

Compared to previous generations, the new models move to a modular, standardized I/O plane built on OCP 3.0 slots across the family, with up to 5 slots per node on the 5500 and 9500 (+1 reserved for expansion) and 3 per node on the 1500 (+1 reserved). The modules are tool-less hot-swap and replace the proprietary SLIC carrier used in Gen 2. Just as importantly, the I/O fabric jumps to PCIe Gen 5 on the newer models, substantially increasing the per-lane bandwidth available to each card and dramatically raising the networking ceiling. At launch, card options include 4× 32/64 Gb FC, 4× 1/10 GbE, 4× 10/25 GbE, and 2× 100 GbE, with 200/400 GbE Ethernet and 128 Gb FC listed as future I/O card releases. Dell is also strengthening network security, as all Fibre Channel cards will support EDIF (Encrypted Data-in-Flight) through a forthcoming non-disruptive software release. The net result is up to 40 network ports per appliance, double the previous generation and roughly 11% more port density than the Gen 2 controller layout.

The controller layout has also been rethought. On Gen 2, the top controller was inverted relative to the bottom one, which made servicing awkward and increased the risk of pulling the wrong controller or grabbing the wrong component during a hot swap. On Gen 3, both controllers are oriented the same way, and either controller can be released using the two handles and levers at the sides of the chassis, sliding out as a single 1.5U tray. The change is small on paper but meaningful for serviceability, particularly in racks where access from above is constrained or where a tech is working under pressure.

PCIe signaling plays an important role in the new PowerStore Elite, drawing on design elements from current-generation PowerEdge servers. When Dell started offering Gen5 E3.S support on platforms such as the PowerEdge R770 or PowerEdge R7725, it was decided to discontinue the use of PCIe switches on the drive backplane. Older models, such as the PowerEdge R760 with a 24-drive backplane, used a PCIe switch to enable all drive lanes, which increased complexity and cost and reduced drive performance. On models using E3.S, configurations with up to 16 SSDs allocated 4 PCIe lanes per drive, while configurations with up to 40 bays allocated 2 PCIe lanes per drive. Dell applies the same philosophy to the new PowerStore Gen 3 models, with each controller using just 2 PCIe lanes to communicate with each dual-ported E3.S SSD. The remaining PCIe Gen5 lanes are then used for inter-node communication and rear I/O connectivity. Nearly every PCIe lane inside the chassis is utilized, down to the remaining PCIe Gen4 lanes off the CPU chipset, which feed OCP slots that don’t require high bandwidth.

Inter-Node Interconnect

The inter-node fabric is one of the bigger jumps from Gen 2. The 5500 and 9500 run up to 200 GbE RDMA between controllers (the 1500 uses 100 GbE RDMA), compared to just 2× 10 GbE on Gen 2. The links are cable-free, midplane-routed point-to-point connections dedicated to write ingest. Pictured below is one of the 200GbE internal interconnect modules on the 9500.

Importantly, the new interconnect layer is CPU-agnostic and decoupled from processor generations and vendors, positioning the chassis to accept upgrades to future CPU platforms across the unit’s lifecycle without disturbing the drives or re-engineering the fabric.

Cache and persistent memory

PowerStore Gen 2 uses front U.2 NVRAM drives for write cache persistence, reducing storage capacity by 4 slots. Gen 3 introduces Software-Defined Persistent Memory (SDPM) instead: standard DDR5 DRAM is presented to the OS as an ACPI-compliant NVDIMM-N, and on power loss, a BIOS SMI handler de-stages the volatile state to the M.2 SSD drive, backed by a hold-up battery subsystem. Each PowerStore Gen 3 array offers substantial lithium-backed power. On the 5500 and 9500 models, each controller has two 54Wh batteries (216Wh total per chassis) while the 1500 model has one per controller (108Wh total per chassis).

Pictured below is the PowerStore 9500 controller with its M.2 boot drive and two battery packs that keep the system powered long enough to commit the state to disk in the event of a power loss.

On the PowerStore 1500, we can see the single-socket layout and a single battery pack to match. The same SDPM architecture is in play, just sized appropriately for the smaller controller.

Power and Management

Power and management have been re-platformed onto Dell’s standard server components. The BMC moves from the legacy EMC GEM controller to a storage-tuned iDRAC, aligning PowerStore with the rest of the Dell server portfolio in terms of serviceability. PSUs and battery backup units are now standard PowerEdge parts rather than custom EMC hardware, which should mean better support operations, less downtime, and broader supply. The hold-up power subsystem covers CPUs, DIMMs, drives, fans, and the iDRAC itself, keeping them powered long enough to de-stage the volatile state to disk on a power loss.

PowerStore Gen 3 Performance

Dell shared preliminary numbers comparing Gen 3 to Gen 2. Treat them as directional, but they line up with what the hardware uplift would suggest, given the move to the latest Intel x86, DDR5, PCIe Gen 5, and the 200GbE RDMA fabric between controllers. Compared to the prior generation, Dell targets up to 3x higher IOPS on 8K mixed workloads, up to 3x higher throughput on 1MB sequential reads, and up to 3x higher throughput on 1MB sequential writes, with meaningful latency improvements on both reads and writes. For an independent comparison, Principled Technologies ran the 9500 against a comparable all-NVMe competitor (unnamed in the report) using Vdbench. On an enterprise OLTP-with-analytics workload, the 9500 delivered 834,558 IOPS, compared to the competitor’s 357,427, a 2.33x advantage.

The latency picture is the same shape. At a 310,000 IOPS target for a mixed small-block database workload, the 9500 held at 0.44ms while the competitor sat at 1.22ms, a roughly 64% reduction.

Data efficiency rounds out the comparison and is the one that matters most as NAND pricing climbs. On a dataset built for 2:1 compression and 2.5:1 deduplication at an 8KB dedupe unit, the 9500 hit 6.6:1 overall reduction. The competitor came in at 2.76:1 on the same data.

Conclusion

PowerStore Gen 3 is the most significant release since the platform debuted in 2020, and it’s the kind of generational reset that will force the rest of the enterprise storage array market to respond. Dell rebuilt the chassis and modernized the drive form factor, cache architecture, inter-node fabric, I/O plane, and management stack in one massive step. Every one of those choices is forward-looking, which is what makes the ten-year Lifecycle Extension story credible rather than aspirational.

The design wins are easy to enumerate. Forty E3.S bays in 3U with no slots burned on cache. A 200 GbE RDMA midplane that’s CPU-agnostic and ready for whatever silicon comes next. SDPM in place of NVRAM drives, which both frees front bays for data and removes the dependency on a single persistent memory technology. iDRAC, PowerEdge PSUs, and Dell battery backup units in place of legacy EMC parts, which means PowerStore now benefits from the same serviceability and supply chain as the rest of the Dell enterprise portfolio. And the unified-by-default approach, paired with the ability to support TLC or QLC media in a single model without a performance penalty, simplifies the buying decision in ways that matter for platforms that must serve diverse workloads in the same stack.

We’re looking forward to spending more time with the platform in our lab and seeing how the Gen 3 hardware comes together with everything PowerStoreOS 5.0 brings on the software side. The autonomous data path work, log-structured metadata for high-capacity QLC, I/O-level telemetry, and dynamic block-and-file resource sharing are all the types of enhancements that compound over time on a chassis with this much headroom. It’s that combination, hardware and software moving forward together, that earns this release the Elite name.

PowerStore Product Page

This report is sponsored by Dell Technologies. All views and opinions expressed in this report are based on our unbiased view of the product(s) under consideration.

The post Dell PowerStore Gen 3: Inside the Most Aggressive Enterprise Storage Reset in Years appeared first on StorageReview.com.

Dell Technologies has introduced a broad infrastructure refresh spanning storage, servers, cyber resilience, and private cloud automation, positioning the portfolio around a common enterprise problem: modern AI and high-performance workloads are scaling faster than many existing data centers were built to support.

The announcement combines a new flagship PowerStore platform, a sizable PowerEdge server update, tighter cyber-recovery integration, and an expanded automation stack aimed at private cloud and distributed infrastructure. Taken together, the release is less about a single product launch and more about Dell tightening the link between performance, density, recovery, and operational simplicity across the data center.

PowerStore Elite

Dell’s headline storage launch is PowerStore Elite, a new high-end version of the PowerStore platform for organizations that need more performance and capacity without a disruptive migration. Dell says it delivers 3x the performance and density of prior generations and scales to 5.8PB of effective capacity in a single 3U appliance, backed by a 6:1 data reduction guarantee.

PowerStore Elite continues Dell’s shift toward modular upgrades instead of forklift refreshes. Drives, controllers, and networking are field-upgradable, allowing in-place hardware modernization. Dell is also moving to industry-standard E3 flash to improve cost efficiency per workload while aligning with newer enterprise flash supply chains and denser drive designs.

Introducing 18th-Gen PowerEdge

On the compute side, Dell introduced its 18th-generation PowerEdge portfolio, which the company says delivers up to 70% better performance and 13:1 consolidation. The refresh spans air-cooled and liquid-cooled systems and targets AI, HPC, enterprise consolidation, and space-constrained deployments.

For higher-density AI and HPC environments, the new Dell PowerEdge M9825 pairs 6th Gen AMD EPYC processors with a liquid-cooled design housed in factory-integrated IR7000 racks. This is intended for customers pushing beyond the practical limits of traditional air cooling, particularly in environments where rack-level thermal planning and deployment predictability matter as much as raw performance.

Dell is also expanding its PCIe-based AI server options with the PowerEdge XE5845 and XE7845, both air-cooled platforms designed for next-generation GPU deployments. These systems target organizations that want AI acceleration without immediately adopting more specialized, liquid-cooled infrastructure.

For more conventional enterprise workloads, Dell introduced the PowerEdge R9825 and R9815, built around 6th-gen AMD EPYC processors. These systems are positioned as high-core-count air-cooled platforms, reaching up to 256 cores per system while increasing I/O bandwidth for demanding workloads. The key point is that Dell is trying to deliver denser compute in standard data center environments without forcing cooling retrofits.

Dell also previewed the PowerEdge R9810, a high-end single-socket 2U platform based on Intel’s next-generation server processor, Diamond Rapids. Dell says the platform will offer double the memory bandwidth, larger cache capacity, and up to 50% more cores, with PCIe expansion aimed at consolidation-heavy use cases.

At the lower end of the footprint spectrum, Dell introduced the 1U PowerEdge R8815 and R6815, both using 6th Gen AMD EPYC to collapse traditional dual-socket footprints into more efficient single-socket systems. That matters for customers focused on software licensing, power, and cooling efficiency rather than simply chasing maximum socket count.

Dell rounded out the AMD-based lineup with the PowerEdge R7815, R7815xd, and R7825. These systems address flexible PCIe Gen6 configurations, storage-dense designs, and dual-socket scale-out requirements for virtualization and analytics. Collectively, the server refresh shows Dell leaning harder into single-socket efficiency where it makes sense, while still reserving dual-socket and liquid-cooled designs for higher-end consolidation and AI workloads.

PowerProtect One

Cyber resilience is another major part of the announcement. Dell launched PowerProtect One, a platform that unifies PowerProtect Data Manager for protection orchestration and PowerProtect Data Domain for protection storage under a single control plane.

This is a practical step to reduce operational sprawl in backup and recovery environments. Instead of managing data protection software, backup storage, and cyber recovery workflows as separate administrative layers, Dell packages them into a unified operating model with centralized visibility and third-party support. Dell says this can cut management overhead by 50% while preserving Data Domain’s data reduction and large-scale recovery capabilities.

Cyber Detect

Dell also announced Cyber Detect integration for both PowerStore and PowerMax. The feature brings AI-driven ransomware detection closer to the storage layer itself, where Dell says it inspects data at the byte level and has been trained on thousands of ransomware variants.

According to Dell, the platform can identify the last known clean copy with 99.99% accuracy, a capability designed to accelerate recovery and reduce the guesswork that often slows response during an active incident. Extending that intelligence into primary enterprise storage is notable because it shifts ransomware analysis earlier in the data lifecycle, rather than relying exclusively on downstream backup or vault workflows.

Automation Platform

Dell’s management story centers on the Dell Automation Platform, which becomes the common control layer for private cloud deployments and future AI-driven operations. The broader strategy is to give customers a more unified way to deploy infrastructure, automate lifecycle tasks, and apply AIOps-style telemetry analysis across compute, storage, and networking.

Within that framework, Dell Private Cloud is positioned as a disaggregated alternative to traditional HCI-based deployments. The platform supports cloud stacks from Broadcom, Microsoft, Nutanix, and Red Hat, with automated lifecycle management and independent scaling of compute and storage. Dell says this model can deliver up to 65% cost savings compared with HCI, although the practical value will depend on the customer’s architecture and licensing assumptions.

New ecosystem updates include support for VMware Cloud Foundation 9.1, Microsoft Azure Local, and Dell PowerStore integration with Nutanix AHV. Those additions matter because they make the platform less about a single software stack and more about offering Dell infrastructure as a common base for multiple private cloud operating models.

Dell also updated its distributed infrastructure story with Dell Distributed Private Cloud, the new name for Dell NativeEdge. The platform is aimed at edge and remote environments. It adds support for two-node high-availability clusters, automatic failover, enhanced VM live migration, built-in zero-trust security, and zero-touch endpoint support. For distributed retail, branch, manufacturing, and field deployments, the emphasis is on resilience and lower-touch operations rather than central data center scale.

Agentic AI

Dell is also moving deeper into AI-assisted infrastructure operations. The company said the Automation Platform will add agentic AI capabilities later this year through a personalized generative interface designed to adapt to how teams build and manage infrastructure. Integrated with Dell AIOps, the model is intended to turn telemetry into recommended or automated actions while keeping the customer in control.

That strategy extends into Dell Automation Studio, a premium set of capabilities built on the Automation Platform. Automation Studio is designed to help organizations create AI-driven workflows across compute, storage, and networking using familiar tooling and operational processes. In practical terms, Dell is trying to package infrastructure automation as an extensible, full-stack layer rather than a set of isolated admin utilities.

Availability

Dell provided the following availability windows:

• Dell PowerStore Elite: July 2026
• Dell PowerEdge M9825, R9825, and R9815: 2H 2026
• Dell PowerEdge XE5845 and XE7845: Q1 2027
• Dell PowerEdge R9810: 2027
• Dell PowerEdge R8815, R6815, R7815, R7815xd, and R7825: 2027
• Dell PowerProtect One: available now
• Dell Cyber Detect for PowerStore: Q3
• Dell Cyber Detect for PowerMax: 2H 2026
• Dell Private Cloud with VMware VCF 9.1 support: June 2026
• Dell Private Cloud with Microsoft Azure Local: June 2026
• Dell Private Cloud with Nutanix and PowerStore support: July 2026
• Dell Distributed Private Cloud: available now
• Dell Automation Platform agentic AI capabilities: later this year
• Dell Automation Studio: June 2026

The post Dell Expands PowerStore, PowerEdge, PowerProtect, and Automation Portfolio for AI-Era Data Centers appeared first on StorageReview.com.

Backblaze’s Q1 2026 Network Stats report shows a slower winter period for neocloud and hyperscaler traffic, with activity beginning to rise again in March. The report examines network-level infrastructure data across Backblaze’s environment, providing context on how data movement is changing as AI-related workloads continue to influence cloud and storage usage patterns.

Since the launch of B2 Overdrive in April 2025, Backblaze has been tracking traffic between its storage layers and neocloud environments used for processing, inference, and modeling. In Q1, hosting and ISP traffic remained close to historical norms; CDN traffic increased over the winter months; and both neocloud and hyperscaler traffic followed a quieter winter pattern before trending upward toward the end of the quarter.

Where Backblaze sent and received the most traffic

The first set of heatmaps compares total bits transferred by Backblaze region and network type in Q1 2026 against Q4 2025. The pattern shows the US-West remaining the most active region for ISP-regional traffic, which Backblaze said was expected given the region’s larger infrastructure footprint and its connections to internet exchanges.

The quarter-over-quarter change was more visible in CDN traffic. While neocloud and hyperscaler activity slowed during the winter period, traffic to CDN partners increased across US-West, US-East, and EU-Central. That shift suggests more data was moving through content delivery networks during the quarter, even as AI-adjacent neocloud and hyperscaler traffic cooled from the previous quarter’s higher levels.

Data Transfers With the Most Magnitude (bits per IP Address)

Backblaze’s next view looks at “magnitude,” or the amount of data transferred per IP address. It is a useful way to separate broad, distributed traffic from heavier point-to-point flows. When a large amount of traffic is distributed across many IPs, it is generally easier to balance the load across the network. When a large amount of traffic is concentrated across fewer IPs, it becomes more challenging from a network engineering standpoint because individual flows carry more weight.

Even though total neocloud traffic declined during the winter months, the bits-per-IP view shows that neocloud transfers remained highly concentrated. That reflects how GPUs and compute clusters tend to move data: when ingesting datasets or producing outputs, they can push high-bitrate traffic through a relatively small number of endpoints. Backblaze said the strongest concentration remained around its US-East cluster, with additional increases showing up in US-West and EU-Central, setting up a closer look later in the report at where neocloud traffic is coming from geographically.

How Many Unique Addresses Backblaze Interacts With

The unique-address view adds another layer to the traffic picture by showing how many distinct IP addresses Backblaze interacted with across each network type. In this case, the Q1 2026 heatmap looks very similar to Q4 2025, which supports the idea that the underlying dataset remained consistent even as traffic volumes shifted during the winter period.

US-West continued to show the highest overall uniqueness, largely because it is Backblaze’s most mature region and supports a wider mix of data centers, workloads, and ISP-regional traffic. Neocloud traffic looked different, with fewer and more persistent endpoints involved. That fits the pattern Backblaze has been describing throughout the report: AI-related storage and compute flows often move large amounts of data between stable endpoints, creating the kind of “elephant flows” that stand out more clearly when traffic is measured by concentration rather than just total volume.

Seasonal Change in Traffic Flows

Backblaze’s summary view shows how the winter slowdown changed the overall traffic mix in Q1 2026. With neocloud and hyperscaler traffic easing from the previous quarter, CDN traffic became a much larger share of total network activity, rising from roughly 20% in Q4 2025 to 32% in Q1 2026.

Localized ISP-regional traffic also grew as a share of the total, increasing from 21.5% to 27.8%. At the same time, neocloud and hyperscaler traffic together fell from 36.4% in Q4 2025 to 25.5% in Q1 2026. AI-adjacent traffic did not disappear, but it accounted for a smaller share of Backblaze’s network activity during the quieter winter period, while CDN and regional ISP traffic filled the gap more.

Backblaze’s report then shifts from network type to geography, examining where traffic is concentrated across Backblaze’s infrastructure.

In March 2026, Backblaze added geographic information to its dataset for the first time, allowing the company to break down traffic concentration by location and network type. The analysis looks at three views: global traffic by country, country-level traffic excluding the United States, and traffic across U.S. states.

Highest Concentration of Traffic by Network Type (Countries)

The first geographic heatmap shows traffic concentration by network type across the top 20 countries in March 2026. Across neocloud, hyperscaler, and CDN traffic, the United States stands out clearly as the largest concentration point in Backblaze’s dataset.

That concentration may reflect a mix of Backblaze’s own infrastructure footprint, with US-West and US-East serving as two of its largest deployments, and the broader shape of the AI infrastructure market. The U.S. remains a major hub for data center capacity, so it is not surprising that network-level traffic tied to cloud, CDN, and neocloud activity would cluster heavily there as well.

Highest Concentration of Traffic by Network Type (Countries, Excluding US)

With the United States removed from view, the second heatmap provides a clearer picture of where international traffic is concentrated. The Netherlands stands out for CDN traffic, which Backblaze links in part to its connectivity with AMS-IX, the Amsterdam Internet Exchange. That reflects a broader difference in European network design, where local internet exchanges often play a larger role than major Tier 1 transit providers because of cost, routing preferences, and regional network politics.

Other international patterns also come into focus in the ex-U.S. view. Singapore shows notable CDN activity, while Germany appears more prominently in hosting traffic. The neocloud category is more scattered, with visible concentrations in Finland, Brazil, France, and Canada. That spread suggests AI-related data movement outside the U.S. is not centered on a single market but is beginning to appear across several regions with meaningful cloud, compute, or connectivity footprints.

The U.S. state-level heatmap further narrows the geographic view and shows neocloud traffic is heavily concentrated in California. That lines up with the broader pattern in the report, where AI-related data flows tend to cluster around regions with dense compute, cloud, and connectivity infrastructure.

Hyperscaler traffic shows a more expected split, with California and Virginia standing out. Virginia’s presence is especially notable because of the Ashburn-Reston corridor, one of the country’s major cloud and data center hubs. CDN traffic, meanwhile, is more concentrated within Backblaze’s footprint, particularly in its US-West region, its largest and longest-running cluster. That makes it more likely to serve older, longer-lived content from those sites, giving the region a stronger role in CDN-related traffic.

Highest Concentration of Traffic by NetworkType (By State)

The U.S. state-level heatmap further narrows the geographic view and shows neocloud traffic is heavily concentrated in California. That lines up with the broader pattern in the report, where AI-related data flows tend to cluster around regions with dense compute, cloud, and connectivity infrastructure.

Hyperscaler traffic shows a more expected split, with California and Virginia standing out. Virginia’s presence is especially notable because of the Ashburn-Reston corridor, one of the country’s major cloud and data center hubs. CDN traffic, meanwhile, is more concentrated within Backblaze’s footprint, particularly in its US-West region, its largest and longest-running cluster. That makes it more likely to serve older, longer-lived content from those sites, giving the region a stronger role in CDN-related traffic.

Backblaze also covered how neocloud and hyperscaler traffic behave over time, and why those categories are harder to plan for than more predictable network types such as CDN, hosting, and ISP-regional traffic. They indicated that neocloud and hyperscaler flows were bursty and high-magnitude, meaning they can move large amounts of data through a smaller number of endpoints. That makes them more demanding from a network engineering perspective, especially compared with traffic that is spread across many sources and destinations.

The newer charts showed several patterns:

• Neocloud and hyperscaler traffic remained more volatile than other categories. Backblaze saw a burst of activity from August through December 2025, followed by a slower winter period and then a renewed increase in high-magnitude neocloud traffic in March 2026.
• Neocloud activity was still strongest in US-East, but March showed a wider spread. Earlier traffic was heavily concentrated, while the March data showed neocloud activity extending more visibly across US-West, US-East, and EU-Central. Backblaze said it will be watching whether that spread continues or narrows in future reports.
• Hyperscaler traffic also slowed in the winter, especially in January. Unlike neocloud traffic, though, hyperscaler patterns remained more consistently visible in US-East from month to month.
• CDN, hosting, and ISP-regional traffic were more stable. These categories showed occasional spikes, including stronger CDN activity in September and some hosting increases in May and October 2025, but the overall pattern was easier to model. Because this traffic tends to involve many IPs communicating with many destinations, it is generally easier to balance across the network.
• ISP-regional traffic was the clearest example of predictable demand. Backblaze tied this category more closely to consumer-driven workflows, which tend to produce steadier patterns than AI-related compute and storage activity.

For Backblaze’s network engineering team, the split creates two different planning models. Neocloud and hyperscaler traffic requires capacity planning for sudden bursts, including large bandwidth additions in 100G and 400G increments, stronger inter-switch capacity within data centers, and private network-to-network connections with selected partners where appropriate. CDN, hosting, and ISP-regional traffic, by contrast, follow steadier growth curves that are easier to forecast.

Geography is becoming a bigger part of that planning as well, as Backblaze said demand is especially concentrated in the United States, with California, Virginia, Illinois, and Georgia standing out in the data. The company is still cautious about drawing firm quarter-over-quarter conclusions. Still, the added views make the contrast clearer: neocloud and hyperscaler traffic is more concentrated, more dynamic, and more operationally demanding than the steadier traffic patterns Backblaze sees from CDN, hosting, and regional ISP activity.

Backblaze Q1 2026 Network Stats Report

The post Backblaze Q1 2026 Network Stats: Neocloud Cools, CDN Climbs, Geography Comes into View appeared first on StorageReview.com.

TrueNAS has introduced the V160, a new addition to its enterprise appliance portfolio designed to support larger, more dynamic workloads. The platform targets organizations that balance performance requirements with cost control, especially as flash pricing volatility continues to affect infrastructure planning.

The V160 is built on dual fifth-generation TrueNAS controllers powered by AMD EPYC processors, with PCIe 5.0 connectivity and up to 768GB of DDR5 memory per controller. The system uses a hybrid architecture that combines high-capacity HDD tiers with NVMe flash and a large adaptive cache layer. TrueNAS reports up to 60 GB/s of throughput, driven by memory bandwidth, and up to 24 TiB of cache.

The hybrid design lets administrators tune the balance between NVMe and SAS HDD media across 24 internal bays and supports expansion to more than 1,400 drives in a single system. Additional scale-out options include up to six NVMe flash shelves or fourteen 102-bay SAS HDD shelves, enabling configurations with up to 20 PiB of flash or more than 35 PiB of HDD capacity. The platform does not impose capacity-based licensing, allowing organizations to scale storage without incremental software costs tied to capacity growth.

From a platform perspective, the V160 consolidates file, block, and S3-compatible object storage into a single system. This unified approach aims to reduce operational overhead from managing multiple storage silos, tools, and support models. The system supports a broad virtualization and container ecosystem, including VMware, Proxmox, Hyper-V, Xen, OpenShift, and Kubernetes. It includes high-availability and failover capabilities designed for large-scale virtual machine deployments.

Capability	TrueNAS V160
Raw Capacity	20 PiB NVMe, 35PiB HDD
Throughput	Up to 60 GB/s
Network	4x 100/200, 2×400 GbE, 4x16Gb FC, 2x32Gb FC
Hybrid Cache	24 TiB
RAM	Up to 768GB DDR5
High Availability	> 99.999% Uptime

The system is also optimized for high-throughput workloads such as media production and AI pipelines. For media environments, including 4K and 8K video workflows, the platform supports real-time editing and ingest without relying on proxy workflows. In AI and machine learning use cases, NVMe tiers can be used for active model serving, while HDD tiers provide lower-cost capacity for training datasets and archival data.

Access	Protocols
File	SMBv2, SMBv3, NFSv3, NFSv4 w/RDMA
Block	iSCSI, iSER, FC, NVMe-oF/RDMA
Object	S3-Compatible with Immutable Locking

Data-efficiency features are built into the platform through TrueNAS Adaptive Compression and Fast Deduplication. Compression is applied selectively to reduce capacity consumption without affecting throughput on incompressible data, while deduplication targets redundant data before it is written to disk. These capabilities are included in the base system rather than as licensed add-ons.

The V160 runs TrueNAS Enterprise 25.10, with all major features enabled by default. These include snapshots, replication, multiprotocol access, and integration with TrueCloud backup. TrueNAS maintains a seven-year lifecycle for enterprise deployments, with no required hardware refresh cycles or additional feature licensing during that period. The company positions this as a predictable cost model driven by architecture and media flexibility rather than incremental licensing.

At the filesystem level, the V160 uses OpenZFS, enabling data portability across TrueNAS systems and other OpenZFS-compatible platforms. This approach avoids proprietary data formats and helps organizations retain control over data placement and migration strategies.

The TrueNAS V160 is available immediately. TrueNAS also indicated that its next major software release, TrueNAS 26, is currently in beta and expected to reach enterprise availability later in 2026.

The post TrueNAS Expands Enterprise Portfolio with V160 Hybrid Storage System appeared first on StorageReview.com.

Dell Technologies has announced PowerMaxOS 10.4, the latest update to its flagship mission-critical storage platform. The release focuses on measurable performance improvements, enhanced ransomware detection, expanded replication capabilities, and tighter integration with VMware and Red Hat OpenShift.

Performance and Efficiency Improvements

PowerMaxOS 10.4 delivers up to 25% faster read response times for SRDF-protected workloads, addressing a key requirement for environments such as Oracle, SAP, Salesforce, and Epic. The improvement is tied to optimizations in replication-aware I/O handling, benefiting organizations operating in synchronous or asynchronous disaster recovery configurations.

Dell also positions the update as a cost efficiency play for the latest PowerMax 2500 and 8500 systems. The updated node-pair architecture is designed to increase IOPS density while reducing the total cost of ownership. This aligns with enterprise demand for scaling performance without proportional increases in footprint or power consumption.

Cyber Resilience and Data Protection

Security enhancements are a core component of the 10.4 release. PowerMaxOS now includes Advanced Ransomware Detection, which is designed to identify anomalous activity earlier in the attack cycle. The platform also expands identity integration by supporting SSO providers, including Okta, PingFederate, and Microsoft Entra ID, as well as private key support for OIDC workflows. These additions support Zero Trust architectures by tightening access controls without introducing operational friction.

Dell continues to build on its multi-site replication strategy. The platform supports a four-site SRDF configuration that combines SRDF/Metro for active-active replication within a region and SRDF/A for cross-region failover. The addition of SRDF/S as a synchronous option within a region provides more flexibility for consistency-sensitive workloads. The architecture is designed to support automated failover, load balancing, and full-scale recovery, with secure snapshots providing an additional layer of data protection.

VMware and OpenShift Integration

PowerMaxOS 10.4 introduces enhancements to simplify the transition from virtualized to containerized environments. VMware virtual machine migrations can be accelerated by up to 10 times using array-based XCOPY in conjunction with the Red Hat Migration Toolkit for Virtualization. This reduces migration windows and minimizes operational disruption during platform transitions.

For container environments, REST API improvements enable up to 7 times faster provisioning of storage clusters for Red Hat OpenShift. The enhancements are intended to streamline infrastructure deployment and reduce time-to-service for developers and platform teams.

Fabric and Infrastructure Readiness

The release adds support for Connectrix 128Gb Fibre Channel switches and directors, based on Broadcom Gen 8 SAN technology. This upgrade increases available bandwidth and port scalability, addressing growing data center throughput requirements. Integration with Connectrix B-Series Gen 8 fabrics also introduces always-on AES-256 encryption, enhanced cryptographic services, and AI-driven management capabilities.

PowerMaxOS 10.4 further aligns with regulatory requirements through FIPS 140-3 Level 2 certification for TLC flash drives, positioning the platform for use in regulated sectors including finance, healthcare, and government.

Availability

The update is available immediately and targets enterprises running latency-sensitive workloads alongside modernization initiatives.

The post Dell PowerMaxOS 10.4 Includes Performance Gains, Expanded Cyber Resilience, and Modern App Integration appeared first on StorageReview.com.

While hyperconverged infrastructure (HCI) has simplified virtualization via streamlined deployments and reduced operational overhead, traditional architectures often struggle with asymmetric scaling. This is particularly evident when storage requirements for large unstructured datasets outpace compute needs, forcing IT teams into inefficient and costly node expansions.

To address this imbalance, Scale Computing and Nexsan have introduced a joint architecture that integrates the SC//HyperCore virtualization suite with enterprise-grade external storage. This combined solution allows organizations to decouple storage growth from compute resources, providing a scalable and cost-effective model for capacity-intensive workloads like video retention, backup repositories, and long-term archives.

Addressing Real-World Infrastructure Constraints

Many IT teams are modernizing their infrastructure while still managing legacy storage investments and growing volumes of unstructured data. Requirements such as long-term video retention, secure backup strategies, and preservation of existing SAN and NAS assets create architectural friction. Traditional approaches often force a tradeoff between adopting fully integrated HCI stacks or continuing with less efficient legacy systems.

The combined Scale Computing and Nexsan approach avoids this binary decision. It enables organizations to retain the simplicity of HCI for core workloads while extending storage capacity through external systems that scale independently.

Architecture Overview

SC//HyperCore provides a tightly integrated virtualization platform with built-in high availability and simplified lifecycle management. It is designed to minimize administrative overhead, particularly in edge and remote deployments.

Nexsan complements this with a portfolio of external storage platforms that support block, file, and object protocols. These systems are designed for capacity scaling, long-term retention, and data protection. Together, the platforms enable a hybrid model in which performance-sensitive workloads remain on-cluster while capacity-heavy datasets are offloaded to external storage.

This separation allows IT teams to align infrastructure decisions with actual workload characteristics rather than forcing all applications into a single scaling model.

Edge and Distributed Use Cases

The joint solution is particularly relevant in edge environments across sectors such as retail, healthcare, manufacturing, education, and government. These deployments often require local compute resources to ensure application performance while supporting centralized data strategies.

SC//HyperCore simplifies operations at remote sites with limited IT presence, while Nexsan platforms handle the associated data growth. This includes centralized archives, backup repositories, and long-term video storage. The result is an edge-to-core architecture that maintains edge simplicity without sacrificing enterprise storage capabilities.

Flexible Storage Integration

A key aspect of the joint approach is support for multiple storage access methods based on workload requirements. Organizations can deploy iSCSI for block-based virtual machine storage, NFS or SMB for file services, and S3-compatible object storage for modern data workflows.

This flexibility enables use cases such as immutable backups, lifecycle-managed archives, and centralized data repositories. It also supports edge-to-core data flows, in which applications run locally while large datasets are aggregated centrally.

Security and Data Protection Considerations

Infrastructure decisions increasingly prioritize cyber resilience alongside performance and capacity. Nexsan platforms incorporate features such as immutable snapshots, object locking, replication, and encryption. These capabilities support secure backup, compliance retention, and rapid recovery workflows.

The Unity NV-Series targets mixed workloads with an emphasis on ransomware resilience, while the E-Series P focuses on dense, high-capacity block storage scenarios such as surveillance. These design points align with environments where data protection and recoverability are critical operational requirements.

Use Cases

The joint solution is best suited for environments with uneven growth patterns and a need for operational simplicity. Common use cases include video surveillance retention, backup and disaster recovery repositories, centralized file services, and long-term archival storage.

It also aligns well with organizations that are modernizing their virtualization while preserving existing storage investments. For channel partners and managed service providers, the architecture supports repeatable solution design that can be tailored to specific vertical requirements.

By separating compute and storage scaling while maintaining a unified operational model, Scale Computing and Nexsan provide a pragmatic approach to modern infrastructure design that reflects how enterprise workloads and data actually grow.

The post Scale Computing and Nexsan Address Asymmetric Growth in HCI Environments appeared first on StorageReview.com.

During GTC 2026, VDURA showcased updates to its Data Platform that improve GPU utilization and storage efficiency in AI environments. The announcement includes the general availability of Remote Direct Memory Access (RDMA), a preview of its Context-Aware Tiering technology, and validated infrastructure setups based on AMD EPYC Turin CPUs and NVIDIA ConnectX-7 networking.

The updates aim to eliminate data movement bottlenecks between GPU clusters and storage and to optimize data placement across storage tiers for large-scale AI training and inference workloads.RDMA Enables GPU-Direct Data Paths

VDURA has added RDMA support across its platform, allowing GPU servers to access storage directly over the network without CPU involvement. This enables GPU-to-storage data transfers that bypass traditional kernel and CPU-mediated paths, reducing latency and increasing throughput.

The implementation integrates with VDURA DirectFlow, the company’s data movement layer, to ensure all GPU server traffic uses RDMA. By eliminating CPU overhead in the data path, compute resources remain dedicated to model training and inference tasks. This approach is intended to sustain higher GPU utilization rates while minimizing pipeline latency in distributed AI clusters.

Context-Aware Tiering Targets Data Placement Efficiency

VDURA also detailed the first phase of its Context-Aware Tiering capability, scheduled for release later this year. This feature introduces automated data placement across storage tiers based on workload behavior and access patterns.

The initial phase extends the DirectFlow buffer into local NVMe SSDs, allowing frequently accessed data to reside closer to compute resources. This reduces dependency on shared or network-attached storage for hot data and improves response times for active workloads.

The platform also introduces KVCache writeback controls, which selectively persist only critical inference data to durable storage. This reduces unnecessary write activity while maintaining persistence guarantees required by production inference pipelines.

Additionally, VDURA is implementing a unified Context Cache Tiering framework that spans DRAM and local SSD. This enables high-speed read and write access aligned with LMCache-class performance, supporting use cases such as long-context LLM inference and retrieval-augmented generation.

VDURA indicated that future phases of Context-Aware Tiering will expand into application-aware data placement, improved cache coherence across nodes, and support for emerging infrastructure components such as NVIDIA BlueField-4 DPUs.

The company also introduced optimized platform configurations combining AMD EPYC Turin processors with NVIDIA ConnectX-7 network adapters. These configurations are designed to complement RDMA-enabled data paths and support high-throughput, low-latency communication between GPU clusters and storage systems.

Full-Stack AI Data Pipeline Focus

VDURA CEO Ken Claffey highlighted the company’s AI storage platform, which spans the entire data hierarchy from memory to long-term storage, and emphasized its performance. He said the platform uses RDMA for direct, CPU-free data access and features Context-Aware Tiering to position data across storage tiers. Claffey noted that these innovations help organizations support larger models, handle more inference requests, and scale AI infrastructure while meeting production AI reliability requirements.

The combined approach is intended to support larger model sizes, increase inference throughput, and improve infrastructure efficiency while maintaining reliability requirements for production AI deployments.

Availability

RDMA is now available on the VDURA V5000 and V7000 platforms. Context-Aware Tiering Phase 1 is expected to reach general availability later in 2026, with early access programs currently underway.

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Everpure is aligning its FlashBlade//EXA platform with NVIDIA’s evolving AI Factory architectures while previewing a new automation layer called Everpure Data Stream. The announcement extends Evergreen//One support to EXA and introduces a service designed to streamline data movement across AI pipelines, targeting one of the most common enterprise AI challenges: projects that work in pilot environments but stall before reaching production scale.

FlashBlade//EXA is positioned as the high-performance data backbone for these deployments, supporting large training runs and high-concurrency inference workloads. The upcoming Data Stream service focuses on automating data ingestion, preparation, and delivery to GPU infrastructure, reducing the operational complexity that often slows AI programs as they move from experimentation to production environments.

EG1 for AI now extends from FlashBlade//EXA to deliver the performance, scale, and throughput needed for larger training runs and high‑concurrency inference. Everpure Data Stream, entering beta later in 2026, is designed to automate data movement from ingestion to model execution, reducing manual pipeline work and operational delays that often slow down AI projects.

Kaycee Lai, Everpure’s AI Vice President, frames the problem as treating AI as “just another workload” rather than as a data‑centric, continuous system. She highlighted that Everpure positions its stack to collapse data silos and move AI programs from experimentation to repeatable production outcomes, enabled by predictable performance and operational flexibility.

Benchmark‑Proven AI Storage and Data Path

For AI deployments, storage and data infrastructure must keep high‑value GPUs running near full utilization. Everpure is aligning FlashBlade//EXA with NVIDIA’s modular STX reference architecture to support next‑generation AI Factory designs built on the Vera Rubin platform.

The combined architecture integrates EXA’s scalable file and object performance with STX components, such as BlueField-enabled storage controllers and context memory architectures. The goal is to optimize the entire AI data pipeline: data preparation, feature and embedding creation, and long‑context inference. Special emphasis is placed on context memory because large-scale, agentic, multi-step reasoning systems rely on quick access to extensive context windows and history. The EXA/STX design addresses these giga‑scale inference demands by delivering sustained bandwidth and minimizing tail latency.

Recent industry benchmarks validate the platform’s behavior under realistic, high‑concurrency AI workloads. The benchmarks include:

• SPECstorage Solution 2020 AI_Image: FlashBlade//EXA achieved the highest score recorded for the SPEC Storage AI_Image benchmark, powering 6,300 simultaneous AI jobs. This result illustrates the system’s ability to support large numbers of concurrent training and preprocessing tasks at full performance, an increasingly common pattern in multi‑tenant and multi‑team AI environments.
• MLPerf‑aligned GPU Utilization and Throughput: Internal, model‑driven workloads aligned with MLPerf show that FlashBlade//EXA can transfer data nearly twice as fast as its closest competitor while using less than half the storage footprint of a rack. In tests, the platform maintained over 90% GPU utilization across large H100 clusters. This suggests the storage system is unlikely to be a bottleneck, allowing expensive accelerators to stay busy as datasets and models grow. EXA’s design scales linearly, maintaining this utilization as more compute and storage are added.

Everpure is also expanding NVIDIA‑Certified Storage (NVCS) validation to FlashBlade//EXA. This effort provides a clearer baseline for compatibility and performance and serves as a stepping stone to the NVCS “NCP” certification level, aligned with NVIDIA Cloud Partner reference architectures. For enterprises standardizing on NVIDIA-focused AI solutions, this type of storage certification helps reduce integration risk and makes it easier to adopt reference designs.

Automating AI Data Orchestration

High storage performance alone does not guarantee AI success if data pipelines into the AI stack remain fragmented and manual. Everpure Data Stream is introduced as an orchestration layer that automates key steps from data ingestion through preparation and delivery into GPU infrastructure.

The service focuses on curating and orchestrating “AI‑ready” data so that training and inference systems are continuously fed with current datasets without requiring heavy operational intervention. The intent is to shorten the time from the initial experiment to a stable production run by reducing ad hoc scripting, manual data staging, and repeated engineering workarounds for dataset refreshes.

An AI Data Platform (AIDP) for Everpure Data Stream, co-engineered with Supermicro, offers a compact reference design for organizations seeking a smaller initial footprint. This combination integrates Supermicro’s server and accelerator hardware with Everpure’s software-defined storage layer, providing a ready-made solution for deploying a data plane that supports both training and inference pipelines.

As part of this AIDP strategy, Everpure also supports accelerated platforms, including the NVIDIA RTX PRO 6000 Blackwell Server Edition, and plans to extend support to the RTX PRO 4500 Blackwell Server Edition. These configurations target customers who need strong inference and edge or departmental training capabilities without having to immediately invest in large data center GPU clusters.

Continuous Data Optimization

Everpure builds its platform around the idea that AI infrastructure isn’t just a one-time investment but an ongoing process of data improvement and performance testing. In this view, AI readiness isn’t just about deploying technology but involves a continuous cycle of collecting new data, retraining or tuning models, and verifying performance as workloads change.

By integrating FlashBlade//EXA, Evergreen//One’s consumption model, Data Stream automation, and alignment with NVIDIA STX and NVCS certifications, Everpure aims to help organizations move ready from isolated AI pilots to repeatable, production‑grade AI factories, while maintaining focus on GPU utilization and operational efficiency.

Internal MLPerf component measurements support some of these claims, although they were not submitted as official MLPerf results. From a technical perspective, the key points are the demonstrated concurrency under SPECstorage AI_Image, the reported GPU utilization figures in H100 environments, and the move toward fully validated NVIDIA‑aligned reference architectures.

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Everpure has expanded its Enterprise Data Cloud strategy by adding ActiveCluster support for file workloads. This new capability aims to provide policy-driven availability and mobility across the entire storage fleet. It ensures that data remains accessible as it moves between environments without interrupting applications.

Targeting Legacy Constraints for AI and Unstructured Data

The growth of AI has made large-scale processing of unstructured data a priority. However, many companies still use storage systems designed before flash, cloud, and AI became common. These outdated systems often lack sufficient throughput to keep modern GPUs fully utilized. This results in underutilized accelerators and longer timeframes for gaining insights.

Traditional designs also reinforce rigid data silos. Policies related to protection, placement, and availability often apply to individual arrays rather than to the datasets themselves. This complicates workflows across sites and makes it difficult when workloads need to move. Additionally, human-driven failover and migration processes can lead to errors, be slow, and be difficult to standardize, especially at scale.

Everpure positions ActiveCluster for file as a solution to these issues. The aim is to transition from array-centric configurations to data- and policy-centric operations that align with the needs of the AI era.

ActiveCluster for File: Fleet-Wide High Availability and Mobility

ActiveCluster for file is the newest feature added to the Everpure Platform, enhancing the company’s synchronous high-availability capabilities for file services. Integrated with Everpure Fusion and part of the Purity operating environment, this feature enables organizations to set availability and mobility policies once and apply them across all participating file systems.

Instead of managing separate high-availability setups for each array or site, administrators can centrally define workload-level SLAs and data mobility policies. The platform automatically enforces these policies across the entire network, reducing manual configuration work and helping standardize operations as environments grow.

A key focus is on removing the need for dedicated hardware pairs or fixed-topology designs. ActiveCluster for file is built to operate across “any array, any time,” using policy and SLA definitions to determine where data should be stored and how it should fail over.

Policy-Driven Operations and Continuous Access

With ActiveCluster for files, setup and management are based on policies and standards rather than low-level replication constructs. The initial configuration is easier for administrators because they can apply consistent templates across workloads, simplifying the onboarding of new applications and sites.

Once policies are established, the system provides continuous access even during outages. Files stay online during failures, with ActiveCluster ensuring availability across participating arrays. The goal is to keep systems operational during array, site, or infrastructure events without disrupting access to application data.

Fleet-wide file mobility is another essential feature. Workloads can shift across the entire fleet in response to policy triggers, performance requirements, or SLA targets, without manual intervention for each migration. This helps operators rebalance workloads, address capacity or performance hotspots, and place data closer to compute resources that support AI and analytics.

By integrating high availability and mobility with a cloud-like model, ActiveCluster for file aims to enable autonomous operation within defined SLAs. Policies govern data location, protection, and movement, and the platform consistently enforces them.

Standardization at Scale

Standardized, policy-driven behavior is essential to the design. Workload-level SLAs are established once and automatically enforced across all relevant systems. This method simplifies scaling infrastructure, as new arrays and sites can implement existing policies rather than requiring custom configurations.

This shifts the discussion from array-specific replication features to fleet-level data services. The emphasis is on how quickly workloads can scale, how consistently SLAs are met, and how effectively the platform supports AI and unstructured-data pipelines across multiple locations.

Availability

ActiveCluster for file is expected to be generally available in Q2 2026. It will be offered as a non-disruptive upgrade within the Everpure platform’s Purity operating environment, requiring no new hardware and no expected downtime for existing deployments.

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CTERA has introduced Fusion Direct, a federated data architecture designed to collapse the long-standing gap between enterprise file systems and object storage. The new offering extends the CTERA Fusion family, which already includes CTERA Fusion Gateway, and is positioned as a core component of the CTERA Intelligent Data Platform. The goal is to present a single, high-performance data fabric that can serve both human-centric file workloads and machine-driven AI pipelines without duplicating data or refactoring applications.

Unifying File and Object Under One Namespace

Enterprise IT teams have traditionally been forced to operate two separate storage domains. NAS systems provide SMB and NFS access for user collaboration and legacy or line-of-business applications. Object storage, accessed typically via S3, is used for large-scale, cloud-native, and analytics workloads. Bridging these environments has often meant standing up parallel infrastructures, copying data between them, or relying on gateways that translate file calls to object APIs. Those translation layers can introduce additional latency, complexity, and operational risk, especially at scale.

Fusion Direct targets that problem by exposing a single federated global namespace in which files and objects coexist natively. Data can be written to the platform as files and read back as objects, or written as objects and read as files. The system supports full bidirectional read and write behavior without converting files into proprietary object chunks or routing access through a separate translation gateway. CTERA is explicit that there is no file-to-object conversion bottleneck and no proprietary encapsulation scheme in play.

From an access perspective, existing enterprise applications and users continue to operate over SMB and NFS as they do today. At the same time, AI training clusters, HPC environments, and cloud-native services can access the same datasets via S3 and S3 over RDMA, which is designed to deliver line-rate throughput to GPU clusters and other high-performance compute environments.

Leveraging Existing Object Stores and Distributed Edge

An important design point is the ability to attach existing S3 buckets directly to the Fusion Direct data fabric. Rather than migrating or rehydrating object data into a new system, organizations can present their current object storage namespaces as part of the global file/object space. Once attached, the objects in those buckets can be accessed as standard files across edge locations and multi-cloud deployments.

This approach allows IT teams to expose object data as files to users and applications globally, while still presenting it natively as S3 to AI and analytics workloads. It also reduces the need for duplicate infrastructure that would otherwise be deployed in multiple regions or edge sites to stage or reformat data. The net effect is a simpler storage footprint for distributed datasets spanning multiple geographies.

Architecture for AI-Era Performance

CTERA Fusion Direct leverages CTERA’s existing Intelligent Data Platform stack and is backed by U.S. Patent 12,007,9521. The architecture emphasizes simultaneous support for collaborative file workloads and high-throughput, machine-scale data consumption.

One core capability is native zero-copy access. Data written to the CTERA platform as files is immediately available as standard S3 objects, without any secondary copies or background conversions. Conversely, S3 buckets can be connected in place, and their contents become immediately addressable as files in the global namespace. This is intended to avoid both latency and storage overhead associated with duplicate copies or intermediate caches.

High-speed file streaming is another focus area. Large media assets, training datasets, and other capacity-heavy content can be streamed directly from object storage to file-based applications. This approach eliminates the need for bulk local downloads or staging steps, which can slow workflows and consume additional storage at the edge or in compute clusters.

On the performance side, Fusion Direct exposes native objects in ways that support S3-over-RDMA and GPU-direct access patterns. For AI clusters, that means GPUs can read and write data at or near wire speed from object-backed datasets without additional protocol translation in the data path. This is particularly relevant for training and inference jobs constrained by I/O throughput rather than raw compute.

CTERA also calls out data sovereignty considerations. Because data resides in standard S3 buckets without proprietary wrapping or gateways that own the metadata, organizations retain control of their information across on-premises deployments and public clouds. The architecture is intended to minimize data-layer lock-in and preserve flexibility as infrastructure strategies evolve.

Collapsing Human and Machine Data Silos

CTERA CEO Oded Nagel says the main barrier to enterprise AI adoption isn’t data shortage but the challenge of using data effectively. He highlights that separating data for human use from data for machine analytics creates friction. Maintaining separate environments and datasets slows AI deployment. Nagel proposes combining these into a single platform supporting SMB/NFS and S3 over RDMA, giving enterprises a direct path from raw data to AI-ready datasets. A unified platform can help organizations better utilize data and stay competitive in a market driven by machine learning and automation.

Availability

CTERA Fusion Direct is available now as part of the CTERA Intelligent Data Platform and is positioned as a core component of the broader CTERA Fusion family for file and object unification. For more information, please visit CTERA.

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Nimbus Data introduced FlashMax as its next-generation multiprotocol all-flash platform for modern data centers. Positioned as a successor to the company’s FlashRack line, FlashMax combines PCIe-based expansion, rack-level resiliency, and next-generation data reduction while presenting block, file, and object services within a single namespace.

Thomas Isakovich, founder and CEO of Nimbus Data, says traditional enterprise storage has become overly complex, increasing costs and reducing efficiency. He explains that Nimbus Data’s FlashMax product changes the technical design (architecture) and the business model of enterprise storage, providing a flash-based data platform focused on simplicity, easy scalability, and long-term cost efficiency (durable economic advantage).

FlashMax consolidates major storage protocols into a single system and flexible capacity pool. This includes NVMe-over-Fabrics (TCP and RoCEv2), Fibre Channel, iSCSI, NFS, SMB, and S3. By allowing block, file, and object storage to coexist in one namespace, the platform can reduce data silos and simplify capacity expansion without requiring separate arrays for different access methods. Connectivity scales up to 400GbE and supports 64G Fibre Channel, targeting performance-driven environments such as virtualization, containers, databases, analytics, and data warehousing, digital media workflows, and large unstructured data repositories.

FlashMax applies hardware-accelerated block-level deduplication and compression to reduce raw capacity requirements and associated rack space and power demands. Nimbus Data also emphasized the use of industry-standard NVMe SSDs rather than proprietary flash modules, positioning the platform as a way to avoid lock-in while improving capacity affordability and supply availability.

System	F500	F700	F900
Base SSDs / Raw Capacity	24 x SSDs (up to 3 PB)	24 x SSDs (up to 3 PB)	24 x SSDs (up to 3 PB)
Max SSDs / Raw Capacity (w/Expansion)	48 x SSDs (up to 6 PB)	120 x SSDs (up to 15 PB)	168 x SSDs (up to 21 PB)
Dual IOCs	Integrated in Base	Integrated in Base	External 2 x 2U
Connectivity
Network Ports (max per IOC)	3 x 100G Ethernet, or 6 x 25G Ethernet, or 4 x 32G Fibre Channel	4 x 400G Ethernet, or 8 x 200G Ethernet, or 16 x 64G Fibre Channel	4 x 400G Ethernet, or 8 x 200G Ethernet, or 16 x 64G Fibre Channel
Built-in Ports (per IOC)	2 x 10GbE SFP+	2 x 10GBASE-T	2 x 10GBASE-T
Expansion Chassis Support	Optional, up to 1 x E240	Optional, up to 4 x E240	Standard, up to 6 x E240
Performance
Throughput	Up to 30 GBps	Up to 100 GBps	Up to 100 GBps
Latency	As low as 100 µsec	As low as 80 µsec	As low as 80 µsec
IOps	Up to 2.2M (4 KiB)	Up to 6.8M (4 KiB)	Up to 6.8M (4 KiB)
Expansion & Features
Expansion Architecture	DirectLinkTM PCIe expansion cards and FlashMax E240 PCIe expansion chassis
Key Features	Redundant PCIe IOCs, dual power/cooling modules, 24 x SSDs, 2U, -500 W
Protocol Support	NVMe-oF (TCP and RoCEv2), iSCSI, Fibre Channel, NFS, SMB, S3, AFP, FTP, TFTP
Storage IO Controller (IOC)	Dual active/passive IOCs with patented architecture (US Patent 9,268,501)
Management Ports (per IOC)	GbE Mgmt, BMC, Console, USB
Redundant Hot-swap Components	IOCs, SSDs, power supplies, and cooling fans
Dimensions
Base System Rack Space	2U – 4U	2U – 10U	6U – 16U
Base System Depth	21.2 in or 539 mm	34.4 in or 874 mm	21.2 in or 539 mm
Base System Weight (maximum)	71.6 lbs or 32.5 kg	88.7 lbs or 40.2 kg	48.8 lbs or 22.1 kg
Power
Voltage	100 – 240 VAC	200 – 240 VAC	100 – 240 VAC
Frequency	48 – 62 Hz	48 – 62 Hz	48 – 62 Hz
Base System Power Consumption	-600 W (900 W max)	-1100 W (2000 W max)	-1400 W (2200 W max)
Environmental & Compliance
Ambient Temperature	Operating: 5 to 40 °C, Non-operating: -20 to 60 °C
Relative Humidity	Operating: 10% to 80%, Non-operating: 8% to 95% (non-condensing)
Altitude	Operating: -50 to 3000 m, Non-operating: -100 to 12,192 m
Shock & Vibration	Operational Shock: 5G for 11ms, 1/2 sine wave pulse Operational Vibration: 0.15G at 5-500 Hz Non-operational Shock: 10G for 11ms, 1/2 sine wave pulse Non-operational Vibration: 0.5G for 5-500 Hz
Agency Approvals	CE Mark, EN55022/EN61000 Class A, FCC Class A, Canadian IECS-003, VCCI Class A, ISO 9002 manufacturing
Warranty & Support	Up to 10-year comprehensive, including 24×7×365 support, rapid parts replacement, with optional media retention

DirectLink PCIe Expansion for High-Density Scale-Up

FlashMax introduces DirectLink, a native PCIe interconnect architecture that attaches expansion capacity directly to dual controllers via dedicated PCIe bandwidth. Nimbus Data positioned this approach as an alternative to legacy expansion shelf designs that rely on expanders and daisy-chained topologies, which can introduce oversubscription and stacking latency. With DirectLink, the company states FlashMax can scale beyond 20PB raw capacity and up to 100PB effective capacity through data reduction, with additional scale potential as SSD densities increase. Overall, FlashMax is positioned as a mass-capacity alternative to scale-out storage that reduces reliance on additional storage nodes and cluster fabrics, and reduces capacity overhead often associated with erasure coding.

Write-Through Architecture and Rack-Level Resiliency

FlashMax is built on a patented parallel write-through architecture intended to avoid controller-to-controller cache mirroring. Writes commit directly to flash rather than DRAM, which Nimbus Data positions as a way to improve resiliency by reducing cache-related complexity and eliminating destaging steps. The platform also supports rack-level failure resilience within a single system, with a single FlashMax deployment spanning multiple racks and remaining operational even in the event of a total rack failure. Built-in synchronous mirroring spans racks while presenting a single redundant namespace, delivering high availability without deploying duplicate arrays.

Enterprise Data Services with Per-System Licensing

FlashMax systems are centrally managed with Omni, Nimbus Data’s monitoring, telemetry, and API-driven automation platform. Nimbus Data lists a full set of enterprise services, including immutable snapshots, remote replication, end-to-end checksums, RAID protection, hardware encryption, and ransomware protection. The company also highlighted per-system software packaging, with features included rather than licensed per terabyte, positioning the model as a way to avoid capacity-based feature costs and maintain predictable economics as deployments scale.

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Pure Storage has rebranded as Everpure, positioning the move as a shift from reshaping enterprise storage to defining broader data management outcomes. Alongside the name change, Everpure has signed a definitive agreement to acquire 1touch, a data intelligence and orchestration company focused on delivering a unified view of enterprise information. The combination is intended to strengthen Everpure’s roadmap for secure, accessible, intelligent data ready to perform in AI-driven environments.

Accelerating Data Management Innovation in the AI Era

Everpure is framing the current market as an AI-driven inflection point where existing infrastructure limitations are increasingly exposed. Many enterprises still operate with siloed datasets, manual governance and movement processes, and architectures that do not scale cleanly for AI workflows. These constraints slow time-to-insight, increase operational risk, and limit the ability to operationalize AI beyond pilots.

To address these issues, Everpure is advancing its Enterprise Data Cloud (EDC) architecture, powered by the Everpure Platform (formerly the Pure Storage Platform). The company describes EDC as a unified, virtualized cloud of data governed by an intelligent control plane. The emphasis is on policy-based, global dataset management that reduces manual configuration and operational friction. The goal is to simplify data management and make it more agile, improving efficiency across environments and use cases.

Extending the Everpure Platform With 1touch Data Intelligence

The planned 1touch acquisition is designed to add data discovery and semantic context directly into the Everpure Platform. Everpure expects 1touch to enable discovery, classification, contextualization, and data enrichment across datasets and environments, including SaaS deployments and edge locations. In practical terms, this expands the platform from managing and protecting data to also understanding it, which is foundational for AI readiness and for improving downstream data usability.

Ashish Gupta, CEO and president of 1touch, emphasized the importance of proper controls and semantic context for AI data, describing data as its lifeblood. He highlighted the partnership with Everpure to remove barriers preventing enterprises from realizing full ROI from their data. Gupta went on to explain that the collaboration aims to expand the Everpure platform to enhance contextual intelligence, enabling quick, reliable transitions of AI projects from pilot to production.

Trading and Transaction Details

Everpure will begin trading under the name “Everpure” on the New York Stock Exchange on March 5, 2026, while retaining the existing ticker symbol (NYSE: PSTG). The 1touch transaction remains subject to customary closing conditions and is expected to close in Q2 FY27. Financial terms were not disclosed.

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