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Phison and Sandisk deploy different tactics to smash GPU memory wall

By
Chris Mellor
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Analysis. GPUs are hitting a memory bottleneck as model sizes outpace onboard capacity. Since GPU memory isn’t scaling fast enough to meet demand, companies like Phison and Sandisk are turning to virtual RAM (VRAM) alternatives – Phison via software, and Sandisk with hardware – using NAND flash as a GPU memory cache.

Phison’s aiDAPTIV+ software is quicker to implement than Sandisk’s hardware, speeds up AI model training by avoiding external storage token access, and enables smaller GPUs to take on larger jobs. Sandisk’s High Bandwidth Flash (HBF) promises much higher speed, but will require semiconductor-level hardware and firmware developments for each GPU manufacturer. 

As we understand it, the Phison software is middleware that runs on a GPU server, with onboard GPUs and a CPU, and sets up a virtual memory pool spanning the GPU’s own memory, be it normal GDDR or High Bandwidth Memory (HBM), CPU DRAM, and an SSD.

When an AI model is loaded, aiDAPTIV+ analyzes its memory needs and slices the data into component parts that are either hot (placed in GPU memory), warm (assigned to CPU DRAM), or cool (assigned to SSD). The SSD is a fast Phison SLC (1bit/cell) drive such as its AI100E M.2 product.

The per-data slice needs change as the AI model runs and aiDAPTIV+ software moves data between the three virtual memory tiers to keep the GPUs busy and avoid token recomputation.

This enables a smaller number of GPUs, with inadequate amounts of their own memory, either HBM or GDDR, for a large model. Phison claims its system can support models with up to 70 billion parameters with a sufficiently large VRAM pool. It means on-prem AI systems can operate larger models for training that would otherwise be impossible or need to be expensively submitted to GPU server farms run by CoreWeave et al. It also means that smaller, less powerful GPUs, and also smaller edge GPU server systems, can run larger inference jobs that would otherwise be infeasible. Think of Nvidia RTX 6000 Ada or Jetson platforms. 

There is no standard GPU HBM/GDDR interface and the Phison software has to be deployed in custom engagements with GPU server and system suppliers. This is done so as to achieve data movement across the three VRAM tiers without modifying the AI application (e.g. PyTorch/TensorFlow). System vendors have access to Phison’s AI100E SSD, middleware library licenses, and support to facilitate system integration.

The list of Phison partners includes ADLINK Technology, Advantech, ASUS, Gigabyte, Giga Computing, MAINGEAR, and StorONE. 

Phison engineers developed aiDAPTIV+ in-house because it couldn’t cover the expense of a full high-end model training system. CEO K. S. Pua says in a video: “My engineer leaders came to me. They asked me to pay them few million dollars to get the machine to do our in-house training, to lower our loading in the human resource to improve the cycle time, to improve efficiency. But I told them, a few million dollars; I’m not able to afford to pay for it. So they go back to their lab, they start to think how to reduce the barrier. Those are smart people. They found a solution to use Phison proprietary enterprise SSD to make it into the systems making the LM training able to execute.”

Get an aiDAPTIV+ solution brief document here.

Sandisk HBF

Sandisk’s option requires close co-development with GPU suppliers because it is built like HBM. This has DRAM die stacks layered above a logic die and connected to it by Through Silicon Via (TSV) connectors. The combined stack is itself connected to a specially designed interposer component that links it to a GPU:

HBF uses the same architecture, with a stacked layer of NAND dies connected by TSVs to a bottom logic layer. This is affixed to an interposer and so connected to a GPU. But it isn’t that simple. HBF augments HBM. So the GPU already has an interposer connecting it to an HBM stack. Now we have to add an HBF stack and extend the interposer to cope with that as well as the HBM. Then there needs to be a memory controller that moves data as required between the VRAM HBM and HBF components. The resulting system would look like this:

There is no CPU DRAM or SSD involved here. As we understand it, a GPU server using an HBF system would require custom designs tailored to each GPU vendor and product family. This is not a plug-and-play system and the engineering cost would be higher. The payoff would be much higher memory capacity and speed than that possible with an aiDAPTIV+ system. The data in the VRAM would always be much closer to the GPU than with Phison’s scheme, providing lower latency and higher bandwidth.

Think of Phison’s aiDAPTIV concept as enabling smaller GPUs to act like bigger ones with more memory, putting AI training in reach of medium-sized businesses and small-scale edge systems. Sandisk’s HBF enables large GPU servers, hamstrung by memory limitations, to train the largest models by expanding their memory capacity to a high degree. 

The Phison and Sandisk technologies are different horses for different courses and can co-exist.

Storage news ticker – July 25

By
Chris Mellor
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Screenshot

A 45Drives four-part video series, led by Chief Solutions Architect and Ceph Foundation board member Mitch Hall, documents the design, build, tuning, and real-world benchmarking of an all-NVMe Ceph cluster – powered by Stornado F16 servers. Early performance tuning results measured so far include:

  • Single‑threaded sequential write: +53 percent (1.3 → 2.0  GBps)
  • Single‑threaded sequential read: +183 percent (2.4 → 6.8  GBps)
  • Four‑threaded sequential write: +50 percent (3.6 → 5.4  GBps)
  • Four‑threaded sequential read: +113 percent (4.5 → 9.6  GBps)
  • Single‑threaded random write: +98 percent (14.9 → 29.6  kIOPS)
  • Single‑threaded random read: -38 percent (74.2 → 45.8  kIOPS) 

A coming white paper from 45Drives will cover overall cluster specs and architecture, system‑level tuning, Ceph tuning, and which tunings map to which real‑world workloads. The first video is available now here. The others are coming.

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Cyber-protector Acronis has named Metrofile Cloud as its premier Disaster Recovery (DR) partner in Southern Africa. Metrofile will integrate Acronis Cyber Protect Cloud into its product lines.

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NoSQL database supplier Couchbase has announced research finding UK businesses are at serious financial risk from slow AI adoption, with over half of IT leaders estimating losses of as much as 5 percent of monthly revenue due to delays. While 79 percent of UK respondents believe AI offers a competitive edge over larger, slower-moving rivals, nearly a third (30 percent) fear the window for adoption has already closed, signalling growing anxiety about falling behind. UK businesses rank among the most pessimistic globally on this point, behind only India (35 percent) and the US (32 percent).

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Cloud file services supplier Egnyte has achieved FedRAMP Moderate Equivalency status for engagements with U.S. DoD contractors and subcontractors, and is also listed on the FedRAMP Marketplace. This enables Egnyte to deliver EgnyteGov, its secure, AI-powered collaboration offering to U.S. federal agencies and government contractors.

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ExaGrid software v7.3.0 updates include: 

  • Support of Rubrik backup software using the Rubrik Archive Tier or Rubrik Archive Tier with Instant Archive enabled
  • Support of MongoDB Ops Manager
  • Deduplication for encrypted Microsoft SQL Server direct dumps If encryption is enabled in the SQL application (TDE), then ExaGrid can achieve about a 4:1 data reduction with ExaGrid’s advanced Adaptive Data Deduplication technology. If a SQL database is not encrypted, ExaGrid will get between a 10:1 and 50:1 deduplication ratio

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Cyber-threat exposure manager Flare has a commissioned Forrester Total Economic Impact (TEI) study that reveals:

  • 321 percent three-year ROI – with an NPV of $699K, and payback in under six months
  • 25 percent reduced risk of data breaches – avoiding $509K in costs
  • 25 percent productivity gains – equal to $167K in labor savings
  • 31 percent license fee reduction vs legacy solutions

The composite company under Forrester’s review generates US$10B in revenue, with 15K employees and 25 full-time employees in SecOps. The ROI analysis shows Flare not only accelerates threat response and reduces reputational risk, but also surfaces deeper intelligence on malware logs, data leaks, and supply-chain exposure.

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HighPoint will debut the Rocket 7638D at FMS2025. It’s a multi-role PCIe Gen 5 x16 Add-In Card that consolidates external GPU connectivity and internal NVMe storage into a single low-profile MD2 adapter. The Rocket 7638D can support a full-height dual or triple-slot Gen5 x16 GPU in length while simultaneously hosting up to 16 enterprise grade NVMe SSDs, all from a single PCIe slot. The external CDFP port supports full-height, 2-slot/3-slot Gen5 GPUs (up to 370mm in length). Dual internal MCIO ports support up to 16 enterprise-grade NVMe SSDs via industry standard cabling. Read more here.

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HighPoint has new RAID products:

  • Rocket 7604A 4x M.2 PCIe Gen 5 x16 NVMe RAID AIC (FH, HL)
  • Rocket 7608A 8x M.2 PCIe Gen 5 x16 NVMe RAID AIC (FH-FH)
  • RocketStor 6541AW 4x U.2 PCIe Gen 4 x15 NVMe RAID Enclosure
  • RocketStor 6542AW 8x U.2 PCIe Gen 4 x16 NVMe RAID Enclosure

It says its NVMe AICs can directly host up to 16 M.2 or E1.S SSDs, delivering up to 128TB of high-speed storage without the need for internal drive bays or cabling accessories. RocketStor 6500 series NVMe enclosures empower compact server and workstation platforms with up to nearly 1TB of enterprise grade U.2 storage via compact low-profile adapter that can be easily integrated into mini or tower form-factor chassis and 1U/2U rackmounts via industry-standard riser accessories.

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IBM’s Q2 2025 results saw a big jump to $17 billion, up 17 percent from Q1 and 8 percent year-on-year, mostly due to the new Z17 mainframe starting shipping. Storage hardware products are sold through the Distributed Infrastructure part of IBM’s Infrastructure group and also in its Software group. Neither figure is made public. Infrastructure group revenues were $4.1 billion, up 14 percent. Software group revenues were $7.4 billion, up 8 percent. Re storage in the Infrastructure group, CFO James Kavanaugh said: ”While Storage was impacted by the new IBM Z cycle as clients prioritized hardware spend, our early strength in z17 and growth in installed MIPS capacity drives a long-term benefit given the 3-4x Z stack multiplier.” In other words, storage hardware appears to have shown decreased revenues.

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IBM has updated its Storage Virtualize software to v9.1.0 with details in a blog and it adds:

  • Scalability enhancements for FlashSystem grid and partitions. Up to 32 systems can now be added to a FlashSystem grid (up from 8 previously) and each system can now manage up to 32 partitions (up from four previously)
  • Added support for objects and management of partitions
  • Separation of duties for use specific certificates
  • Security enhancements, updated inference engine and reporting of anomalies within native GUI*
  • VASA5 support
  • Various miscellaneous enhancements and continued GUI modernization

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Infinidat CMO Eric Herzog presented his company’s cyber storage security features to a Gartner Security & Risk Management Summit. Download his slides here.

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Keepit offers its own cloud storage backup capability. A blog from CISO Kim Larson describes this and says that testing is the heart of business resilience.

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Kingston has added an M.2 2230 form factor for its NV3 PCIe Gen 4 M.2 SSD, previously only available in the 2280 size. It provides storage expansion from 500 GB to 2 TB for systems with limited space. Powered by a Gen 4×4 NVMe controller NV3 2230 delivers read/write speeds of up to 6,000/5,000 MB/s. It includes 1-year free Acronis True Image for Kingston software, alongside the Kingston SSD Manager application.

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Kingston says its Kingston IronKey D500S hardware-encrypted USB flash drive has received NIST FIPS 140-3 Level 3 validation. It claims D500S is the world’s first and only FIPS 140-3 Level 3 validated drive with a TAA-compliant and trusted supply chain.

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Kioxia is sample shipping 512 Gb TLC chips built from BiCS 9 (218-layer) 3D NAND with mass production in its fiscal 2025 (ending March 31, 2026). They deliver up to 4.8 Gbps under demo conditions and designed to support apps requiring high performance and power efficiency in the low- to mid-level storage capacities. They’ll be integrated into Kioxia’s enterprise SSDs, particularly in ones aim to maximize GPU efficiency in AI systems. 

Kioxia will use BiCS 9 218L flash for high performance at reduced production cost and BCS 10 332L for the expected future demand for larger-capacity, high-performance product. 

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Leil Storage announces SaunaFS 5.0 supports HAMR SMR drives from Seagate. David Gerstein, CTO of Leil Storage, said: “Getting SaunaFS 5.0 to support HAMR drives in general, and HAMR SMR drives specifically, right as they’re hitting the market was a big deal for us – we wanted enterprises of any size to benefit from these massive leaps in storage capacity as soon as possible. This release is all about putting innovative, efficient storage within reach for everyone.”

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Micron is launching a high-density, radiation-tolerant single-layer cell (SLC) NAND product with a die capacity of 256 Gb. It’s the first in a portfolio that will include space-qualified NAND, NOR and DRAM products. It’s available now.

The product has:

  • Extended quality and performance testing, aligned with NASA’s PEM-INST-001 Level 2 flow, which subjects components to a yearlong screening, including extreme temperature cycling, defect inspections and 590 hours of dynamic burn-in to enable spaceflight reliability.
  • Radiation characterization for total ionizing dose (TID) testing, aligned with U.S. military standard MIL-STD-883 TM1019 condition D, which measures the cumulative amount of gamma radiation that a product can absorb in a standard operating environment in orbit and remain functional, a measurement that is critical in determining mission life cycle.
  • Radiation characterization for single event effects (SEE) testing, aligned with the American Society for Testing Materials flow ASTM F1192 and the Joint Electronic Device Engineering Council (JEDEC) standard JESD57. SEE testing evaluates the impact of high-energy particles on semiconductors and verifies that components can operate safely and reliably in harsh radiation environments, reducing the risk of mission failure. This profiling information enables space engineers and architects to design in a way that mitigates the risk and disruption to the mission.

Micron customer partner Mercury Systems already uses Micron memory in its solid-state data recorders (SSDRs) to capture and store mission scientific and engineering data aboard NASA’s Earth Surface Mineral Dust Source Investigation (EMIT), an imaging spectrometer built by NASA’s Jet Propulsion Laboratory and launched to the International Space Station in 2022. 

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There is a 7.3 version of ObjectiveFS which includes new features such as AssumeRole, ListObjectsV2, settable user agent header, and TLS and lease performance improvements:

  • New ListObjectsV2 support (enabled by default on Amazon S3)
  • ListObjects version can be selected using the LISTOBJECTS environment variable
  • New AssumeRole support to get credentials using AWS STS (learn more)
  • New settable user agent header using the USER_AGENT environment variable
  • Added header support for buckets with S3 Object Lock enabled
  • TLS prioritizes AES256 over ChaCha20 to reduce TLS overhead
  • Live rekeying supports switching between manual keys, IAM role and AssumeRole
  • Improved object store cache fetches during startup for very large filesystems
  • Optimized lease performance for a case where multiple nodes create new files in a shared directory tree
  • Added new regions for AWS and GCS

 More information here.

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Sponsored research by Odaseva reveals nearly 40 percent of organizations don’t have backup systems in place, and among those that do, more than two-thirds don’t know how often they test whether their disaster recovery plans actually work. So most enterprises don’t know if their backup data is recoverable when disaster strikes. The survey also found that more than 40 percent of organizations believe their data is more secure when stored independently from their primary cloud platforms, compared to just 23.5 percent who see no concerns with platform-dependent solutions. This survey of 600-plus enterprise architects across 47 countries was sponsored by Odaseva and conducted by Salesforce Ben.

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Ransomware protection is built into every layer of DDN subsidiary Tintri‘s VMstore. With immutable snapshots, granular, per‑VM  recovery that replaces lengthy full-environment restores, real‑time analytics to detect anomalies,  and automated backups and replication, organisations can contain and rebound from attacks swiftly. To close  out Ransomware Awareness Month, Tintri will host a new installment of its Geek Out! Technology Demo Series  titled “Never Mind, It’s Fixed.” Taking place on Wednesday, July 30 at 10 a.m. PT / 1 p.m. ET, this grunge-themed virtual event will explore how Tintri VMstore can help IT teams  automate recovery and minimise downtime. Register to join here.

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Research consultancy ESG has produced a technical validation of Tintri’s VMstore Kubernetes Data Services. ESG analysed Tintri VMstore’s capabilities and how they apply to containerised applications in three distinct use cases: DevOps workflows, unified management and observability, and data protection and disaster recovery. ESG identified several Tintri-specific differentiators that underscore its well-established AI-enabled infrastructure, observability, and management within the container platform space. Find out more here.

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Distributed SQL database supplier Yugabyte announced new vector search, PostgreSQL, and multi-modal functionality in YugabyteDB to meet the growing needs of AI developers:

  • YugabyteDB MCP Server for seamless AI-powered experiences in applications
  • Support for LangChain, OLLama, LlamaIndex, AWS Bedrock, and Google Vertex AI
  • Multi-modal API support with the addition of MongoDB API support for scaling MongoDB workloads in addition to PostgreSQL (YSQL) and Cassandra (YCQL)
  • Online upgrades and downgrades across major PostgreSQL versions with zero downtime 
  • Enhanced PostgreSQL compatibility with generated columns, foreign keys on partitioned tables, and multi-range aggregates
  • Built-in Connection Pooling that can support tens of thousands of connections per node.

It says multi-modal API support across YSQL, YCQL, and MongoDB, workloads, and vector indexing and search with a YugabyteDB MCP server means organizations can now build and deploy highly resilient, “ready-to-scale” RAG, and AI-powered applications with 99.99 percent or higher uptime using familiar PostgreSQL and powerful vector search capabilities architected for 1 billion-plus vectors. More info in a blog.

Sandisk assembles advisory board to guide High Bandwidth Flash strategy

By
Chris Mellor
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Sandisk is setting up a Technical Advisory Board to help guide the development and strategy of its High Bandwidth Flash (HBF) technology.

HBF applies the High Bandwidth Memory (HBM) DRAM technology concept to flash with a stack of NAND dies grouped together and connected to host GPU via an interposer unit. This aggregates the IO channels from each stacked die so that the collective data transfer bandwidth to the GPU is a multiple of an individual die’s bandwidth. It uses proprietary stacking with ultra-low die warpage for 16-high configuration and the architecture has been developed over the past year with input from leading AI industry players.

Naturally, NAND access latency time is longer than DRAM so HBF is seen as augmenting HBM with an additional memory tier, and providing greater NAND bandwidth than a traditional SSD. This means it can be viewed as memory/storage tier between HBM and external SSDs providing, for example, a faster checkpoint store during AI training. Having announced HBF technology, Sandisk needs help in bringing it to market.

It has appointed Professor David Patterson and Raja Koduri to its Technical Advisory Board and says they will provide “strategic guidance, technical insight, market perspective, and shape open standards as Sandisk prepares to launch HBF.”

CTO Alper Ilkbahar stated: “We’re honored to have two distinguished computer architecture experts join our Technical Advisory Board. Their collective experience and strategic counsel will be instrumental in shaping HBF as the future memory standard for the AI industry, and affirming we not only meet but exceed the expectations of our customers and partners.” 

That’s the tricky bit – making HBF the future memory standard for the AI industry. Because it uses an interposer that has to be bonded to the GPU, Sandisk’s customer is the GPU manufacturer or a skilled semiconductor package level systems builder. There is an HBM-GPU connection standard effectively designed by Nvidia to which HBM manufacturers Micron, Samsung, and SK hynix adhere. HBM was primarily developed by SK hynix and Nvidia, and Nvidia ensured it was not locked into a single supplier.

Unless Nvidia adopts HBF, Sandisk will be relegated to trying to pick up the minor GPU suppliers, meaning AMD and Intel, and possibly looking to other AI accelerator suppliers as well, such as the hyperscalers with their own chips.

David Patterson

David Patterson is a foundational technology heavyweight. He is Pardee Professor of Computer Science, Emeritus at the University of California at Berkeley, and a Google distinguished engineer, and will lead the Technical Advisory Board toward actionable insights and decisions. He is a prominent computer scientist known for co-developing Reduced Instruction Set Computing (RISC), which revolutionized processor design.

Patterson, we’re told, played key roles in the development of Redundant Array of Inexpensive Disks (RAID), and Networks of Workstations (NOW). He co-authored the seminal textbook Computer Architecture: A Quantitative Approach, and was also awarded the 2017 ACM Turing Award for his contributions to the industry. 

He said: “HBF shows the promise of playing an important role in datacenter AI by delivering unprecedented memory capacity at high bandwidth, enabling inference workloads to scale far beyond today’s constraints. It could drive down costs of new AI applications that are currently unaffordable.” 

Raja Koduri

Raja Koduri is a computer engineer and business executive renowned for leading graphics architecture, with previous positions at AMD as Senior Vice President and Chief Architect, and at Intel as Executive Vice President of Accelerated Computing Systems and Graphics. He directed the development of AMD’s Polaris, Vega, and Navi GPU architectures, Intel’s Arc and Ponte Vecchio GPUs, and spearheaded Intel’s foray into discrete graphics.

In early 2023, he founded a startup focused on generative AI for gaming, media, and entertainment, and joined the Board of Tenstorrent in the AI and RISC‑V semiconductor space. Most recently, he serves as founder/CEO of Oxmiq Labs and co-founder of Mihira Visual Studios and continues to shape graphics and AI innovation through advisory and board roles across the semiconductor industry.

He said: “HBF is set to revolutionize edge AI by equipping devices with memory capacity and bandwidth capabilities that will support sophisticated models running locally in real time. This advancement will unlock a new era of intelligent edge applications, fundamentally changing how and where AI inference is performed.” 

The edge here is small datacenter edge, not the remote office/branch office edge.

A LinkedIn post by Koduri said: “When we began HBM development our focus was improving bandwidth/watt and bandwidth/mm^2 (both important constraints for mobile), while maintaining competitive capacity with the incumbent solutions. With HBF the focus is to increase memory capacity (per-$, per-watt and per-mm2) significantly while delivering competitive bandwidth. Compute(flops) * Memory capacity(bytes) * Bandwidth(bytes/sec) modulates performance of AI models for both training and inference.”

Sandisk’s Technical Advisory Board will include industry experts and senior technical leaders from both within and outside the company. Patterson is ex-AMD and Koduri ex-Intel, with AMD and Intel being the alternative GPU manufacturers to Nvidia. It’s possible that Intel will exit the GPU space as it restructures, leaving AMD as the sole competitor to Nvidia. The lack of an Nvidia representative on the Technical Advisory Board could be seen as concerning from an all-embracing “memory standard for the AI industry” point of view. Were Nvidia to agree there is a need for HBF, its future would be more certain. As it is the TAB could have an uphill struggle. You can check out an HBF fact sheet here.

Backtracking Amazon DynamoDB: Clumio pushes recovery play

By
Chris Mellor
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Clumio Backtrack enables partition-level Amazon DynamoDB recovery back to a point in time with no database reconfiguration.

DynamoDB is AWS’ widely-used and fully-managed NoSQL database service. AWS says it serves hundreds of customers, with table sizes exceeding 200 TB, and processes over one billion requests per hour. Clumio is Commvault’s acquired AWS data protection business with an incremental forever backup model. The company has previously announced Clumio Backtrack for Amazon Simple Storage Service (S3),  which enables customer admins to quickly revert billions of S3 objects – or pieces of data – to a specific version at a specific point and time. Now Backtrack gets extended to cover DynamoDB and do the same thing with table partitions.

Woon Jung, Commvault’s CTO for cloud native stuff, stated: “Clumio Backtrack  removes the friction and risk from database recovery. Now, teams can roll back or restore their data in minutes, not days, and without needing to perform complex, multi-step manual recoveries.

The company says Dynamo deployments contain complex tables, often with multiple partitions within each table, that are constantly updated by applications and microservices. Such tables can contain billions of records and terabytes of data. If there is an outage or other disruption admins would like to recover just the affected partitions but, Clumio says, admins generally have to restore all of data to a new table, copy the impacted items back, and then delete the new table. This can be a slow and tedious process entailing database down time.

Clumio Backtrack for DynamoD screen grab.

Clumio Backtrack fixes it by enabling DynamoDB admins to near instantly revert existing DynamoDB tables to a prior point in time with no reconfiguration needed. They can also recover individual partitions instead of entire tables, cutting both recovery times and recovery costs.

Perhaps we can look forward to Backtrack being extended again and covering AWS’ Aurora relational database.

Clumio Backtrack for DynamoDB is now available globally via the AWS Marketplace and pricing is consumption-based. Find out more about Clumio and its offerings here.

Comprehensive solutions from HPE for safeguarding your data

By
Kevin Cole, director, product and technical marketing, HPE Data Protection
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HPE data protection portfolio looks after your digital assets no matter what threats may come

In today’s digital landscape, organizations face relentless threats and disruptions that can compromise their data integrity and operational continuity. As hybrid IT environments grow more complex and cyberattacks rise, organizations need a robust, agile, and intelligent approach to data protection.

HPE’s data protection solutions are a good basis for a data protection strategy that safeguards critical workloads, ensures cyber resilience, and accelerates recovery across all your environments from on-premises to cloud.

Navigating data protection challenges

Ransomware hit 59% of organizations last year, with 94 percent of those attacks targeting backup environments. They disrupt operations and finances, costing $4.9 million per attack on average.

Companies facing down these threats must also rethink how they protect data as data storage and workloads evolve. Data now stretches across edge devices, datacenters, and the cloud. Workloads such as SaaS, cloud-native apps, AI stacks, and big data analytics have changed the game. More fragmented environments have created serious challenges for data protection, and more stringent regulations have increased the risk of missteps.

Reimagining data protection

HPE’s holistic approach to data protection secures every application while meeting business cost and performance needs. It includes mitigating infrastructure failures, automating protection to reduce human error, and planning for both bad actors and disasters simultaneously.

HPE data protection solutions cover any workload, any location, and any SLA requirement, ensuring recoverability across hybrid and multi-cloud environments.

Strengthen your data protection strategy with HPE

Cyber resilience

This is critical to maintaining business continuity and minimizing data loss when cyberattacks happen. HPE’s cyber resilience capabilities are integral to its holistic approach, providing strong access controls, comprehensive encryption, policy-driven protection, and air-gapped architectures. Real-time anomaly detection and analytics guide protection and recovery decisions, providing swift protective actions and rapid restoration of trusted data.

High performance

Speed is crucial in modern data protection because every minute of downtime can result in lost revenue, damaged reputation, and operational setbacks. With HPE’s high-throughput backup and restore speed, you can quickly recover critical data and resume operations after an incident.

Fast failover and failback ensure minimal disruption, while efficient data reduction optimizes storage and reduces costs. Additionally, HPE’s cyberforensics capabilities enable quick identification of safe data for restoration, further reducing recovery time and ensuring your business stays up and running.

An integrated ecosystem

Managing data security across diverse environments can be a daunting task. Many organizations struggle with fragmented data protection solutions that lead to inefficiencies and vulnerabilities. HPE’s integrated ecosystem addresses these pain points by providing seamless data security from the point of creation (source) to its final destination for backup or long-term retention (target).

With intelligent data movers facilitating secure and efficient transfer in between, you can ensure that your data is protected at every stage. Additionally, HPE integrates best-in-class technologies from strategic software partners such as Commvault, Veeam, and Cohesity to achieve comprehensive data protection and eliminate the complexities of managing multiple solutions.

HPE solutions to secure your business data


HPE offers solutions to address all data protection needs across the data lifecycle, starting with creation of data at the source. This includes HPE Alletra Storage MP B10000 for structured data storage with high availability, multi-factor authentication, encryption, anomaly detection, and immutable snapshots. For unstructured data, HPE Alletra Storage MP X10000 provides highly available, fast unstructured data object storage with data protection accelerator nodes, encryption, immutability, and versioning.

After data is written to storage, continuous data protection with HPE Zerto Software provides ultra-low recovery point objectives (RPO) and recovery time objectives (RTO). HPE Zerto unlocks granular, journal-based recovery of virtualized and cloud applications with features such as real-time anomaly detection, orchestrated failover and testing, and application-centric recovery.Pair HPE Zerto with backup software and infrastructure that extends protection to all applications across your entire data estate. HPE StoreOnce is a high-performance purpose-built backup appliance featuring multi-factor authentication, encryption, immutable backups, and fast, cost-efficient backups and restores. Additionally, HPE also offers integrated solutions with Commvault, Veeam, and Cohesity for backup and cyber resilience no matter where or how your workloads are deployed.

For the ultimate in cyber-protection, the HPE Cyber Resilience Vault enables rapid air-gapped recovery after even the worst cyber-attack. A full-stack solution, it offers isolated and immutable copies of your data in an offline clean room for recovery. It is built atop well-established offerings for storage (HPE Alletra Storage MP), compute (HPE ProLiant), networking (HPE Aruba), and cyber recovery (HPE Zerto).

Lastly, as data ages and the need for long-term retention stretches into years, HPE Cloud Bank Storage and HPE Storage Tape provide cost-effective storage options. HPE Cloud Bank Storage enables long-term cloud storage with multi-factor authentication, encryption, and immutability. HPE Storage Tape serves as a low-cost, air-gapped, offline backup and archive repository with immutability and encryption.

HPE’s data protection portfolio offers a multi-layered, zero-trust approach addressing the evolving challenges faced by modern enterprises.

HPE ensures robust data protection and business resilience by integrating best-in-class technologies to secure every application, meeting business cost and performance needs.

With solutions designed to protect against cyberthreats, disruptions, and regulatory complexities, HPE empowers organizations to safeguard their data and maintain operational continuity in an increasingly complex digital landscape.

Learn more about HPE’s data protection products.

Contributed by HPE.

ExaGrid reports growth in deduped backup biz

By
Chris Mellor
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ExaGrid says it’s had another storming quarter, with the privately held company saying this is its 18th consecutive growth quarter selling tiered deduping backup target appliances that compete with systems from Dell and HPE.

The company’s disk-based appliances have a non-dedupe backup ingest landing zone, for fast restores, and a Repository Tier zone with cluster-wide deduplication. System features include a Retention Time-Lock that includes a non-network-facing tier (creating a tiered air gap), delayed deletes and immutability for ransomware recovery. The business, which did not reveal any figures, claimed it made record revenue in the second quarter, adding over 140 new customers with the average deal size increasing.

Bill Andrews

President and CEO Bill Andrews stated: “Revenue and EBITDA continue to grow year over year. We are the largest independent backup storage vendor, and we continue to add support and integration with more backup applications which offers investment protection to our existing customers and provides even more potential for top line growth.”

“We’ve hit well over 4,700 active customer installations worldwide. ExaGrid continues to have an over 70 percent competitive win rate (74.1 percent in Q2) replacing primary storage behind the backup application, as well as inline deduplication appliances such as Dell Data Domain and HPE StoreOnce.”

The company has been FCF positive, P&L positive, and EBITDA positive for the past 18 quarters and has zero debt. We estimate it had 112 six- and seven-figure deals in the quarter. Half of its business this quarter came from new customers, and over 50 percent of its overall business came from outside the USA. ExaGrid said it is adding over 14 more sales regions worldwide.

Its appliances support more than 25 backup applications and utilities including: Veeam, Commvault, NetBackup, HYCU, Oracle RMAN direct, Rubrik, SQL Dumps direct and HYCU, with support for Cohesity DataProtect due in the first half of 2026.

Recently other backup target appliance companies have been adding all-flash configurations, such as Dell and Quantum. Such all-flash appliances can provide up to 130T TB/hour throughput in Dell’s case which puts ExaGrid in the slow lane with its 20 TB/hour (EX189 product). We would expect ExaGrid to have hybrid flash-disk and/or all-flash appliance developments ongoing with an announcement in the next 12 months and possibly sooner.

Backblaze adds one-click legal hold to enterprise backup service

By
Chris Mellor
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Cloud storage provider Backblaze is providing a simple on/off switch to set up a legal hold on files stored in its vaults.

It has introduced Legal Hold for Backblaze Computer Backup with Enterprise Control to preserve a user’s entire backup, including every historical version captured, with a single click. Legal holds may be necessary when an organization becomes involved in litigation, requiring potentially relevant information to be preserved unchanged for legal proceedings. It can be legally required even when an organization reasonably anticipates litigation. Implementing such legal freezes on stored data and finding out which files are relevant and need to be placed in an immutable state can be time-consuming. It’s extra work and cost, with potential financial penalties for non-compliance.

Gleb Budman, Backblaze
Gleb Budman

Backblaze CEO Gleb Budman stated: “With Legal Hold, information stays secure and immediately accessible when the stakes are highest. Reliable preservation of data reduces exposure to fines and sanctions, giving organizations fast, predictable compliance support without extra software, hardware, or surprise fees.” 

The update features are:

  • Administrators can instantly activate Legal Hold in the Enterprise Control console without additional hardware or software. 
  • Runs silently in the background without downtime, throttling, or notifications. 
  • Offers unlimited version retention without additional fees. 
  • No charge instant file retrieval with available encrypted drive delivery via courier. 
  • Data is secure by default with encryption at rest and in transit, with optional private-key encryption available. 

Data management suppliers such as Datadobi and Komprise offer legal hold functionality for data under their management. Cloud file services supplier CTERA does the same, as do several others.

Legal Hold is available now for all Backblaze Computer Backup with Enterprise Control customers at no additional cost. New users can try it out with a free 15-day trial, accessible here.

SK hynix revenues go sky high with HBM

By
Chris Mellor
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High Bandwidth memory has become a high revenue earner for Korea’s SK hynix as second 2025 quarter revenues jumped 35.4 percent year-on-year to ₩22.23 trillion ($16.23 billion).

There was a ₩7 trillion ($5.1 billion) profit, up 69 .8 percent annually, down 14 percent sequentially, and the company is amassing cash, with its cash and cash equivalents increasing to ₩17 trillion ($12.4 billion) at the end of June, up by ₩2.7 trillion ($1.97 billion) from the prior  quarter. Its debt ratio and net debt ratio stood at 25 percent and 6 percent, respectively, as net debt fell by ₩4.1 trillion ($3 billion), compared with the previous quarter. HBM chips provided 77 percent of its second quarter revenue.

Song Hyun Jong, SK hynix President and Head of Corporate Center, said in the earnings call: “Demand for AI memory continued to grow, driven by aggressive AI investments from big tech companies.” 

SK hynix said its customers plan to launch new products in the second half and iIt expects to double 2025 HBM revenues compared to 2024. Increasing competition among hi-tech companies to enhance AI model inferencing will lead to higher demand for high-performance and high-capacity memory products. Ongoing investments by nations to build sovereign AI will also help, with Song Hyun Jong saying: “Additionally, ongoing investments by governments and corporations for solving AI are likely to become a new long term driver of AI memory demand.” 

AI looks to be the gift that just keeps on giving as AI training needs GPUS with HBM and DRAM, AI inferencing needs servers with GPUs+HBM and DRAM, and AI PC, notebooks, tablets and smartphones will need DRAM and NAND.

The company will start provision of an LPDDR-based DRAM module for servers within this year, and prepare GDDR7 products for AI GPUs with an expanded capacity of 24Gb from 16Gb. HBM4 will come later this year. Song Hyun Jong said: “We are on track to meet our goal as a Full Stack AI Memory Provider satisfying customers and leading market expansion through timely launch of products with best-in-class quality and performance required by the AI ecosystem.”

SK Hynix Q2 cy2025 revenue by product amd application.

NAND looks a tad less exciting, as SK hynix will “maintain a prudent stance for investments considering demand conditions and profitability-first discipline, while continuing with product developments in preparation for improvements in market conditions.” It will expand sales of QLC-based high-capacity eSSDs and build a 321-layer NAND product portfolio.

Kioxia/Sandisk’s BiCS10 NAND has 332 layers with Samsung’s V10 NAND promising 400+, up from its current (V9) 286 layers. Micro is at 276 with its G9 NAND and YMTC at 232 with its Gen5 technology. SK hynix’s Solidigm subsidiary has 192-layer product and looks in need of a substantial layer count jump if it is to stay relevant. It builds its NAND with a different fab and design from SK hynix and, unless it adopts SK hynix 321-layer tech, will have to devize its own at considerable expense. Whichever route it takes, adopt SK hynix tech or go-it-alone, it will have to equip fabs with new tools. Using SK hynix tech would potentially save a lot of development expense and a 321-layer product would give it an up-front 67 percent increase in die capacity over its 192-layer product. 

Overall SK hynix has grown its share of the DRAM and NAND market, with Counterpoint Research estimating it is now neck and neck with Samsung for combined DRAM and NAND revenues in the second quarter at the $15.5 billion mark.

Counterpoint Senior Analyst Jeongku Choi said, “SK hynix saw its largest-ever quarterly loss (₩3.4 trillion or $2.7 billion) in Q1 2023, prompting painful decisions such as production cuts. However, backed by world-class technology, the company began a remarkable turnaround, kick-started by the world’s first mass production of HBM3E in Q1 2024. By Q1 2025, SK hynix had claimed the top spot in global DRAM revenue, and just one quarter later, it is now competing head-to-head with Samsung for leadership in the overall memory market.”

MinIO gives object storage a GenAI upgrade with Iceberg

By
Chris Mellor
-

Interview: MinIO is building a linking layer between its object store and GenAI and that involves embracing structure in the form of Iceberg tables. It also entails building some linkage between them and, up until now, the vector-focused GenAI models and agents.

We talked to MinIO co-founder and co-CEO AB Periasamy and Erik Frieberg, his CMO, to explore this topic. It’s fairly complex and we’ll cover it in two parts, with this being part one. The interview has been edited for brevity.

Blocks & Files: What’s the main thing affecting MinIO presently?

AB Periasamy

AB Periasamy: The best thing that happened to us is GenAI and in every enterprise the budget is now towards GenAI and the race is starting to shift towards data. Who has more data and how to put data to use? That is directly contributing to our company’s growth. Because once you start scaling your data and AI data infrastructure, it points to object store and customers are also looking beyond cloud. Cloud is already behind GenAI and the scale of data is pushing the customers to good private cloud and the private cloud object store, everything is in our backyard. We created that market outside of AWS and that’s contributing to our growth. And you can see the company has been aggressively growing hiring in on all sites, engineering, like marketing, sales, even. We launched a government BU and are launching a partner program. Overall, the company is going through an upgrade and aggressive growth.

Blocks & Files: How many people work at MinIO now?

AB Periasamy: I stop counting, Erik. How much? It is like 150-plus.

Erik Frieberg: I would say 160 something now. It’ll be 170-ish next week.

Blocks & Files: What’s happening with MinIO marketing?

Erik Frieberg

Erik Frieberg: I joined a little while ago to run marketing. The go-to-market organization a month before I joined in November was dramatically different than it is today. I think, that’s no joke, eight or nine times of what it was a year ago.

Blocks & Files: Why the strategy to have that built up so quickly?

AB Periasamy: I made the decisions. I’ll just give the high level view that, even though we are hiring aggressively relative to others, we are very prudent about hiring because, in this AI world, we have to think about hiring very differently. All the junior-level jobs today, machines can do so much better and most other companies hire aggressively and then fire aggressively. 

For us, we treat our team like family and it’s so important to take your time, be prudent about every one of those who you bring on board. Two years from now, five years from now, are they going to be with the family and are they going to be more productive because they can combine themselves with AI? That’s the only hope. Human plus machine is going to be better than human and machine in isolation. 

So be prudent about bringing the right people in who will fill the holes we have inside the organization. That’s how we are looking at it. So, in many ways, I actually look at this as this is not fast enough, but I would rather hire less and not hire anybody than hire aggressively.

Erik Frieberg: I’m going to say three things. One is I would actually say AB probably started hiring too late, so this should have been on the rise already. Second is the difference of having AIStor. I come from an open source background. I was at MongoDB where there was no commercial differentiation and it makes a big difference. There’s always only so much support and services you can sell to customers. 

You’ve got to have differentiated production software that has features that are a catalyst for growth. And that [AIStor] came out in the October-ish time frame and I think people are now really understanding it, seeing the difference. You’re going to see some announcements coming in the future on this, so that’s creating more opportunity. And then like AB said, it’s not a target. It’s not that he’s saying: “Go hire five people in marketing.” He’s saying: “Hey, we need more product marketing now because we have more to talk about.”

Blocks & Files: AB, MinIO is a storage company and for a customer of yours who is using generative AI, large language models and agents, there’s a software stack between the storage layer and their agents and large language models. So where in that stack do you see MinIO’s activities ending? Where’s the border and is it moving up the stack?

AB Periasamy: Yes. In fact, the story we have been telling all along resonates more with the customers now than the past. We always said MinIO object store is a key value store. What is a key value store? It’s a data store. We are closer to Oracle than EMC in the past. The data store is the dumbest of all databases. It is a key value store with get and put objects, but it needs to process data at massive scale without losing a single object. It has to be transaction ACID-compliant, all of that and scale, while it has the simplicity at its roots. So we always thought of ourselves as a database company, except that we are talking about unstructured data. Things are starting to evolve there because of GenAI.

We brought Erik in because he came from MongoDB. Our culture is closer to MongoDB than Pure or NetApp or MCA software company and in modern times [with] GenAI, the scale is much larger. The only way you can go is scale-out and then go software-defined. There are some interesting things that are happening [with] the object store and the layer above Gen AI, where the integration is going on.

The AI is directly talking to the object store through the MCP server. It’s the agents that are interacting with the object store, both in terms of administration as well as the data discovery and dealing with the data itself. There are some interesting challenges that are emerging here. If it’s a spreadsheet, if it’s a CSV file, single object? Say I have a database file, can I analyze this? We are talking about enterprises having 10 petabyte tables; data sitting in Iceberg tables. In the past it was proprietary data sitting in proprietary databases. They are increasingly now moving towards Iceberg open table format.

How can you have large language models understand that scale of data? That’s a bridging factor that we need to work on and those are the areas that we are actually actively investing in as well. The model may still be running on OpenAI or Anthropic, but the enterprise data never leaves their facility. How do they discover the data? Only the metadata or an anonymized form of the intelligence is shared. We are actively working on that part of the stack.

There’s also another interesting twist here that, if it’s unstructured data like blobs, photos, videos, documents, then everybody knows how LLM take advantage of them. The new emerging area is structured data, structured data like Iceberg tables, and at large scale. How can LLMs understand structured data?

Comment 

The point AB is making is that Iceberg tables are structured and it makes no sense to vectorize them as, unlike words or images, they don’t stand alone with dimensional aspects that you can vectorize. There needs to be some intervening logic/abstraction between the tables and the GenAI LLMs and agents that bridges their unstructured data vector focus and the searching of structured data. We’ll explore this more in part two.

The Iceberg age: StarTree latest to adopt popular table format

By
Chris Mellor
-

Iceberg is becoming the lingua franca of datalake table formats with StarTree the latest supplier to embrace it as a real-time backend.

Open source Iceberg is an open table format for large-scale analytics which doesn’t store or execute queries itself, as a traditional database would. It operates as a software layer atop storage systems like Parquet, ORC, and Avro, and cloud object stores such as AWS S3, Azure Blob, and the Google Cloud Store, which handle handles metadata, partitioning, and schema evolution.  Iceberg provides ACID transactions, schema versioning, and time travel, with data querying and processing handled by separate SW such Apache Flink, Presto, Spark, Trino, and other analytics engines. 

The StarTree Cloud is a fully managed, cloud-native platform built on Apache Pinot, a real-time distributed OLAP (Online Analytical Processing) datastore. StarTree Cloud is designed for OLAP and enables low-latency querying (milliseconds) and high-throughput processing (10,000+ queries/second) of large-scale data from streaming sources (e.g., Apache Kafka, Amazon Kinesis) and batch sources (e.g., AWS S3, Snowflake). Now it can be both the analytic and serving layer on top of Iceberg.

StarTree claims Iceberg support can transform it from a passive storage format into a real-time backend capable of powering customer-facing applications and AI agents with high concurrency serving thousands of simultaneous users with consistent speed and reliability. 

Kishore Gopalakrishna on the summit of Half Dome, Yosemite.

Kishore Gopalakrishna, StarTree co-founder and CEO, stated: “We’re seeing explosive growth in customer-facing, and increasingly agent-facing, data products that demand sub-second responsiveness and fresh insights. At the same time, Iceberg is emerging as the industry standard for managing historical data at scale.”

“As these two trends converge, StarTree is delivering unique value by acting as a real-time serving layer for Iceberg empowering companies to serve millions of external users and AI agents securely, scalably, and without moving data.”

Recent Iceberg adoptees include Snowflake, Confluent, AWS with S3, SingleStore, and Databricks. 

Paul Nashawaty, principal analyst at theCUBE Research, said: “Apache Iceberg is rapidly becoming the de facto standard for managing large-scale analytical data in the open data lakehouse—adoption has surged by over 60 percent year-over-year, according to theCUBE Research.”

StarTree asserts that most existing query engines built around Iceberg and Parquet struggle to meet the performance SLAs required for external-facing, high-concurrency analytical applications, and companies have historically avoided serving data directly from their lakehouse. It claims that by combining Iceberg and Parquet open table formats Pinot’s indexing and high-performance serving capabilities, StarTree offers real-time query acceleration directly on native Iceberg tables.

StarTree Cloud graphic.

Unlike Presto, Trino and similar engines, StarTree says it’s built for low-latency, high-concurrency access, integrating directly with Iceberg, boosting performance with features such as:

  • Native support for Apache Iceberg and Parquet in StarTree Cloud
  • Real-time indexing and aggregations, including support for numerical, text, JSON, and geo indexes
  • Intelligent materialized views via the StarTree Index
  • Local caching and pruning for low-latency, high-concurrency queries
  • No data movement required—serve directly from Iceberg
  • Intelligent prefetching from Iceberg, minimizing irrelevant data scans 

Nashawaty reckons: “StarTree’s ability to serve Iceberg data with sub-second latency and without data duplication is a unique and timely advancement. It addresses a critical performance need for accessing historical data in modern data products.”

Support for Apache Iceberg in StarTree Cloud is available today in private preview. For more information, visit www.startree.ai.

Kioxia unveils highest capacity SSD at 245.76 TB

By
Chris Mellor
-

Online storage capacity constraints are melting away with Kioxia announcing the largest capacity SSD at 245.76 TB, opening the door to a 200-plus petabyte rack and a five-rack exabyte configuration.

It complements the already announced 122.88 TB model and is intended for use in data lakes – where massive data ingestion and rapid processing are required – and generative AI, which requires storing large datasets for training large language models (LLMs), as well as the embeddings and vector databases that support inference via retrieval-augmented generation (RAG).

These drives are aimed at very large organizations and hyperscalers that need this level of capacity muscle. 

Neville Ichhaporia, Kioxia
Neville Ichhaporia

Neville Ichhaporia, SVP and GM of the SSD business unit at Kioxia America, said the LC9 series sets “a new benchmark for capacity and innovation in the SSD space. As generative AI reshapes datacenter architecture, we’re delivering the kind of breakthrough technology that enables our customers to optimize their IT infrastructure investments – pushing boundaries in performance, efficiency, and scale.”

Kioxia now has twice as much drive capacity as competitors Phison (122.88 TB) and Solidigm (122 TB), which announced their drives in November last year. Since Sandisk is a NAND fab joint-venture partner of Kioxia, it could also produce a similar-sized SSD to the LC9.

The LC9 drives use Kioxia’s BiCS8 218-layer 3D NAND organized in QLC (4bits/cell) format with 2 Tb dies. These 32 dies are stacked to produce 8 TB of capacity in a compact 154-ball BGA (Ball Grid Array) package. Kioxia says this is an industry-first. 

We imagine that Phison might find scope for parallelism with around 30 of these BGAs needed for a 245.76 TB drive, were it to be minded and able to buy the component chips and produce one.

A Kioxia slide provides LC9 speeds-and-feeds information:

The drives are available in U.2 (2.5-inch), E1.S, and E1.L formats, though maximum capacity is limited to 122.88 TB in the 2.5-inch and E1.S variants.

These SSDs have a PCIe 5×4 interface, or PCIe 5×2 in a dual-port config, along with NVMe. Performance is heavily skewed to reads, with an up to 12 GBps bandwidth versus just 3 GBps for writes. This is not that fast for PCIe Gen 5 SSDs. Kioxia’s own CD9P drive offers up to 14.8 GBps reads and 7 GBps writes but it does use faster TLC NAND.

Phison’s 128 TB Pascari D205V uses QLC NAND, like the LC9, and is faster, delivering up to 14.6 GBps read bandwidth and 3.2 GBps write speeds. The SK hynix PS1012 drive is also a QLC SSD and is slightly faster as well. It pumps out 13 GBps read bandwidth at the 61 TB capacity level. A 122 TB version is due later this year.

Kioxia’s LC9 SSDs

The LC9 has Flexible Data placement support to improve endurance, at 0.3 drive writes per day, and performance. It also has various security features, such as self-encryption, and meets the Open Compute Project (OCP) Datacenter NVMe SSD v2.5 specification, but not all requirements.

We envisage other SSD suppliers announcing their own similarly capacious SSDs and pitching them at the same market soon enough. It’s noteworthy that Pure Storage is introducing 300 TB versions of its proprietary NAND drive, the Direct Flash Module (DFM), and Meta has licensed DFM IP. Both Micron and Kioxia are NAND chip suppliers to Pure.

The pace of SSD capacity expansion is rapid, far outpacing disk drive capacity growth, which is currently in the 32-36 TB area with 40 TB drives forecast. There is no pricing information available for the LC9 so we can’t make a $/TB comparison with HDDs. A rack full of LC9s could potentially significantly outperform disk drives in TCO terms because of the reduced floor and rack space, cooling, and most likely the power they need.

Kioxia’s coming BiCS 10 generation of NAND will feature 332 layers, 52 percent more than BiCS 9. That would enable, in theory, a 3 Tb QLC die and, by extrapolation, a 368 TB SSD. This would have seemed crazy three or four years ago. Such is the way our expectations of what’s normal in the NAND world have changed.

Kioxia LC9 Series SSDs are now sampling with select customers and will be featured at the Future of Memory and Storage 2025 conference, taking place August 5-7 in Santa Clara.

LTO tape market rebounds in 2024 with 176.5 EB shipped

By
Chris Mellor
-
LTO tape
LTO tape

There was 15.4 percent more LTO tape capacity shipped in 2024 compared to 2023 as growth resumed following three years of more or less level pegging after a pandemic dip.

The LTO Program Technology Provider Companies (TPCs) – HPE, IBM, and Quantum – released their annual tape media shipment report, saying a record 176.5 exabytes (EB) of total compressed tape capacity shipped in 2024, assuming a 2.5:1 compression ratio. Similar numbers are available for 2019 to 2023, but not for earlier years. However, the LTO org has previously produced a bar chart showing the 2008 to 2019 history. We have combined the old and new charts to provide a full 2008 to 2024 tape capacity shipped history: 

There was a dip in 2018 due to a patent dispute between the two tape ribbon manufacturers, Fujifilm and Sony, which restricted the global supply of LTO-8 tape media until a cross-licensing deal was agreed between the two.

Capacity shipped then grew in 2019 to 114 EB, but was hit hard by the COVID-19 pandemic in 2020 with a fall to 105 EB. There was a ramp back up in 2021 to 148 EB, but dismal 0.5 percent growth in 2022 to 148.3 EB, and a laggardly 3.14 percent rise to 152.9 EB in 2023. Happily, the need to archive unstructured data has now resumed growing and we have a healthier 15.4 percent growth from 2023 to 2024.

Bruno Hald

Looking ahead to the hoped-for AI archiving demand, Bruno Hald, General Manager, Secondary Storage, at Quantum, said: “Setting a new growth record for the fourth year in a row, LTO tape technology continues to prove its longevity as a leading enterprise storage solution. Organizations navigating their way through the AI/ML era need to reconfigure their storage architectures to keep up, and LTO tape technology is an essential piece of the puzzle for those seeking a cost-friendly, sustainable, and secure solution to support modern technology implementation and the resulting data growth. We look forward to introducing the next iteration of LTO tape technology this year to bring enhanced storage capabilities to the enterprise.” Hald is referring to the LTO-10 format.

The LTO org says in its announcement that tape complements SSD and disk as an archival storage medium “to provide a low-cost, last line of defense against ransomware and other security threats.” And it mentions air-gapping, of course: “Further, offline copies of LTO tapes separate data archives from the connected environment, providing greater assurances for data recovery.”

It does not mention that LTO-10, unlike previous generational LTO changes, does not provide a speed increase over LTO-9, only saying: “LTO-10 … will provide the same blazing speed as LTO-9.” Nor does it mention LTO-10’s loss of backwards-compatibility or initial 30 TB raw capacity rather than the expected 36 TB raw capacity. Setting these relatively minor points aside, the future for LTO tape shipments looks good for the rest of 2025 and 2026. But if Microsoft’s Project Silica or Cerabyte or HoloMem get their various glass tablet technologies productized by 2027, 2028, or 2029, then we might see some real competition for tape developing.

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