Kioxia announced fourth quarter and full fiscal 2024 year results with revenues up slightly but set to decline as rising datacenter and AI server sales fail to offset a smartphone and PC/notebook market slump and an expectation that the future exchange rate will look bad.
Q4 revenues were ¥347.1 billion ($2.25 billion), up 2.9 percent annually, but 33 percent down sequentially, with IFRS net income of ¥20.3 billion ($131.8 million), better than the year-ago ¥64.9 billion loss.
Full fy2024 [PDF] revenues were ¥1.706 trillion ($11.28 billion), up 58.5 percent annually, with an IFRS profit of ¥272.3 billion ($1.77 billion), again much better than the year-ago ¥243.7 billion loss.
Hideki Hanazawa
CFO Hideki Hanazawa said: “Our revenue exceeded the upper end of our guidance. … Demand from enterprises remained steady. However ASPS were down around 20 percent quarter-on-quarter, influenced by inventory adjustments at PC and smartphone customers.” The full year revenue growth “was due to an increase in ASPS and bit shipments resulting fro recovery in demand from the downturn in fiscal year 2023, the effect of cost-cutting measures taken in 2023 and the depreciation of the yen.”
Financial summary
Free cash flow: ¥46.6 billion ($302.6 million)
Cash & cash equivalents: ¥167.9 billion vs year-ago ¥187.6 billion
EPS: ¥24.6 ($0.159)
Kioxia’s ASP declined around 20 percent Q/Q with circa 10 percent decrease in bits shipped. It said it has had positive free cash flow for five consecutive quarters.
It’s NAND fab joint venture partner Sandisk reported $1.7 billion in revenues for its latest quarter, 0.6 percent down Y/Y, meaning that Kioxia performed proportionately better.
Kioxia ships product to three market segments and their revenues were:
SSD & Storage: ¥215.2 billion ($$1.4 billion) up 32.5 percent
Other (Retail + sales to Sandisk): ¥52.3 billion ($339.6 million) up10.7 percent
Smart devices Q/Q sales decreased due to lower demand and selling prices as customers used up inventory. The SSD and storage segment is divided into Data Center/Enterprise, which is 60 percent of its revenues, and PC and Others which is 40 percent. Demand remained strong in Data Center/Enterprise, driven by AI adoption, but Q/Q sales declined mainly due to lower selling prices. There was ongoing weak demand in the PC sub-segment and lower selling prices led to reduced Q/Q sales.
Overall there was strong demand in the quarter for Data Center/Enterprise product with continued softness in the PC and smartphone markets. There was around 300 percent growth in Data Center/Enterprise SSD sales in the year compared to last year.
Kioxia had an IPO in the year and has improved its net debt to equity ratio from 277 percent at the end of fy2023 to 126 percent at fy2024 year end, strengthening its balance sheet. The company has started investing in BiCS 10 (332-layer 3D NAND) technology for future growth. This has a 9 percent improvement in bit density compared to BiCS 8 (218 layers).
The calculations for next quarter’s outlook assumes a minimal impact from US tariff changes, some improvement in smartphone and PC demand, accelerating AI server deployments and strong data center server demand, fueled by AI. However it anticipates a decrease in both revenue and profit on a quarterly basis. The corp believes the main reason for this sales decline will be the exchange rate, with the current assumed rate for June being ¥140 to the dollar. It was ¥154 to the dollar in the fourth fiscal 2024 quarter. A change of ¥1 to the dollar is expected to have an impact on Kioxia’s operating income of approximately ¥6 billion.
With this in mind, the outlook for the next quarter (Q1 fy2025) is revenues of ¥310 billion +/- ¥15 million; $2 billion at the mid-point and a 10.1 percent Y/Y decline. Kioxia says the potential for growth in the NAND market remains strong due to AI, datacenter and storage demand. If only the smartphone and PC/notebook markets would pick up!
Don’t just replace VMware; tune up your infrastructure
Finding a suitable VMware alternative has become a priority for many IT organizations, especially following Broadcom’s acquisition of VMware. Rather than swapping out hypervisors, IT should seize this opportunity to reevaluate and modernize the entire infrastructure, adopting an integrated software-defined approach that optimizes resource efficiency, scalability, and readiness for emerging technologies, such as enterprise AI.
Beyond simple hypervisor replacement
Replacing a hypervisor without addressing underlying infrastructure limitations provides temporary relief. Issues such as siloed resource management, inefficient storage operations, and limited scalability remain, continuing to increase operational complexity and costs.
Adopting a comprehensive software-defined infrastructure ensures seamless integration, flexibility, and efficient scaling—critical factors for handling modern workloads, including legacy applications, VDI, and AI workloads.
Preparing infrastructure for enterprise AI
The rapid adoption of AI increases demands on existing IT infrastructure. AI workloads require extensive computational resources, high-speed data storage access, advanced GPU support, and low-latency networking. Traditional infrastructures, with their inflexible storage, compute, and network architectures, fail to meet these dynamic and intensive requirements.
Due to these software inefficiencies, IT teams are forced to create separate infrastructures for AI workloads. These new infrastructures require dedicated servers, specialized storage systems, and separate networking, thereby creating additional silos to manage. This fragmented approach increases operational complexity and duplication of resources.
A modern, software-defined infrastructure integrates flexible storage management, GPU pooling, and dynamic resource allocation capabilities. These advanced features enable IT teams to consolidate AI and traditional workloads, eliminating unnecessary silos and allowing resources to scale smoothly as AI workloads evolve.
What to look for in software-defined infrastructure
When selecting a VMware alternative and transitioning to a software-defined model, consider the following essential capabilities:
Integrated storage management
Choose solutions that manage storage directly within the infrastructure software stack, removing the need for external SAN or NAS devices. This integration streamlines data management, optimizes data placement, minimizes latency, and simplifies operational complexity.
No compromise storage performance
The solution should deliver storage performance equal to or exceeding that of traditional three-tier architectures using dedicated all-flash arrays. Modern software-defined infrastructure must leverage the speed and efficiency of NVMe-based flash storage to optimize data paths and minimize latency. This ensures consistently high performance, meeting or surpassing the demands of the most intensive workloads, including databases, VDI, and enterprise AI applications, without compromising simplicity or scalability.
Advanced GPU support for AI
Look beyond basic GPU support traditionally used for VDI environments. Modern infrastructure solutions should offer advanced GPU features, including GPU clustering, GPU sharing, and efficient GPU virtualization explicitly designed for AI workloads.
Efficient resource usage
Prioritize infrastructure that supports precise and dynamic resource allocation. Solutions should offer granular control over CPU, memory, storage, and networking, reducing wasted resources and simplifying management tasks.
Efficiency is critical due to the high cost of GPUs. Don’t waste these valuable resources on unnecessary virtualization overhead. To maximize your investment, modern solutions must deliver performance as close to bare-metal GPU speeds as possible, ensuring AI workloads achieve optimal throughput and responsiveness without resource inefficiencies.
High-performance networking
Evaluate infrastructures that feature specialized networking protocols optimized for internal node communications. Look for active-active network configurations, low latency, and high bandwidth capabilities to ensure consistent performance during intensive operations and in the event of node failures.
Global inline deduplication and data efficiency
Ensure the infrastructure offers global inline deduplication capabilities to reduce storage consumption, beneficial in environments with substantial VM or AI workload duplication. Confirm that deduplication does not negatively impact system performance.
Ask yourself when the vendor introduced deduplication into its infrastructure software. If it added this feature several years after the platform’s initial release, it is likely a bolt-on introducing additional processing overhead and negatively impacting performance. Conversely, a platform that launched with deduplication will be tightly integrated, optimized for efficiency, and unlikely to degrade system performance.
Simplified management and automation
Choose solutions that provide comprehensive management interfaces with extensive automation capabilities across provisioning, configuration, and scaling. Automated operations reduce manual intervention, accelerate deployments, and minimize human error.
Enhanced resiliency and high availability
Opt for infrastructures with robust redundancy and availability features such as distributed data mirroring, independent backup copies, and intelligent automated fail-over. These capabilities are critical for maintaining continuous operations, even during hardware failures or scheduled maintenance.
IT should evaluate solutions capable of providing inline protection from multiple simultaneous hardware failures (such as drives or entire servers) without resorting to expensive triple mirroring or even higher replication schemes. Solutions that achieve advanced redundancy without significant storage overhead help maintain data integrity, reduce infrastructure costs, and simplify operational management.
Evaluating your VMware alternative
Transitioning from VMware provides an ideal opportunity to rethink your infrastructure strategy holistically. A well-designed, software-defined infrastructure enables superior resource management, simplified operational processes, and improved scalability, preparing your organization for current needs and future innovations.
By carefully evaluating VMware alternatives through the lens of these critical infrastructure capabilities, IT organizations can significantly enhance their infrastructure agility, efficiency, and ability to support emerging technology demands, particularly enterprise AI.
Don’t view your VMware exit merely as a hypervisor replacement. Use it as a strategic catalyst to modernize your infrastructure. Storage is another key concern when selecting a VMware alternative. Check out “Comparing VMware Alternative Storage” to dive deeper.
Dell is refreshing its PowerScale and ObjectScale storage systems as part of a slew of AI Factory announcements on the first day of the Dell Technologies World conference.
The company positions its storage systems, data lakehouse, servers, and so on as integrated parts of an AI Factory set of offerings closely aligned with Nvidia’s accelerators and providing a GenAI workflow capability as well as supporting traditional apps. PowerScale – formerly known as Isilon – is its scale-out, clustered filer node offering. ObjectScale is distributed, microservices-based, multi-node, scale-out, and multi-tenant object storage software with a single global namespace that supports the S3 API.
Jeff Clarke
Jeff Clarke, Dell Technologies COO, stated: “It has been a non-stop year of innovating for enterprises, and we’re not slowing down. We have introduced more than 200 updates to the Dell AI Factory since last year. Our latest AI advancements – from groundbreaking AI PCs to cutting-edge data center solutions – are designed to help organizations of every size to seamlessly adopt AI, drive faster insights, improve efficiency and accelerate their results.”
As background, Nvidia has announced an extension of its storage server/controller host CPU+DRAM bypass NVMe/RDMA-based GPUDirect file protocol to S3, so that object data can be fed fast to its GPUs using similar, RDMA-based technology. Parallel access file systems like IBM’s Storage Scale, Lustre, VAST Data, VDURA, and WEKA have a speed advantage over serial filers, even scale-out ones, like PowerScale and Qumulo. Dell has responded to this with its Project Lightning initiative.
With these points in mind, the ObjectScale product is getting a denser version along with Nvidia BlueField-3 DPU (Data Processing Unit) and Spectrum-4 networking support. BlueField 3 is powered by ARM processors and can run containerized software such as ObjectScale. Spectrum-4 is an Ethernet platform product providing 400Gbit/s end-to-end connectivity. Its components include a Spectrum-4 switch, ConnectX-7 SmartNIC, BlueField-3, and DOCA infrastructure software.
The denser ObjectScale system will support multi-petabyte scale andis built from PowerEdge R7725xd server nodes with 2 x AMD EPYC gen 5 CPUs, launching June 2025. It will offer the highest storage density NVMe configurations in the Dell PowerEdge portfolio. The system will feature Nvidia-enhanced BlueField-3 DPUs and Spectrum-4 Ethernet switches and provide planned network connectivity of up to 800 Gb per second.
The vendor says ObjectScale will support S3 over RDMA, making unstructured data stored as objects available much faster for AI training and inferencing. It has up to 230 percent higher throughput and up to 80 percent lower latency. It will also provide 98 percent reduced CPU load compared to traditional S3 data transfers, it claims. A fully managed S3 Tables feature, supporting open table formats that integrate with AI platforms, will be available later this year.
PowerScale gets S3 Object Lock WORM in an upcoming release, along with S3 bucket logging and protocol access logging. PowerScale file-to-object SmartSync automates data replication directly to AWS, Wasabi or Dell ObjectScale for lower-cost back up storage, and can burst to the cloud using EC2 for compute-heavy applications.
A PowerScale Cybersecurity Suite is an AI, software-driven product designed to provide ransomware detection, minimum downtime when a threat occurs, and near-instant recovery. There are 3 bundles:
Cybersecurity software for real-time ransomware detection and mitigation, including a full audit trail when an attack occurs.
Airgap vault for immutable backups.
Disaster recovery software for seamless failover and recovery to guarantee business continuity.
Project Lightning is claimed to be “the world’s fastest parallel file system per new testing, delivering up to two times greater throughput than competing parallel file systems.” This is according to internal and preliminary Dell testing comparing random and sequential throughput per rack unit. Dell has not provided specific throughput figures, which makes independent comparison difficult. A tweet suggested 97Gbps throughput. The company says Project Lightning will accelerate training time for large-scale and complex AI workflows.
Dell says Lightning is purpose-built for the largest AI deployments with tens of thousands of GPUs. Partners such as WWT and customers such as Cambridge University are active participants in a multi-phase customer validation program, which includes performance benchmarking, feature testing, and education to drive product requirements and feedback into the product.
Dell is introducing a high-performance offering built with PowerScale, Project Lightning and PowerEdge XE servers. It will use KV cache and integrate Nvidia’s Inference Xfer Library (NIXL), part of Nvidia’s Dynamo offering, making it ideal for large-scale, complex, distributed inference workloads, according to Dell. Dynamo serves generative AI models in large-scale distributed environments and includes optimizations specific to large language models (LLMs), such as disaggregated serving and key-value cache (KV cache) aware routing.
A Dell slide shows Project Lightning sitting as a software layer above both ObjectScale and PowerScale in an AI Data Platform concept:
We asked about this, and Dell’s Geeta Vaghela, a senior product management director, said: ”We really start to see parallel file systems not being generic parallel file systems, but really optimised for this AI use case and workflow.” She envisages it integrating with KV cache. Dell is now looking to run private previews of the Project Lightning software.
Dell says its AI Data Platform updates improve access to high quality structured, semi-structured, and unstructured data across the AI life cycle. There are Dell Data Lakehouse enhancements to simplify AI workflows and accelerate use cases, such as recommendation engines, semantic search, and customer intent detection by creating and querying AI-ready datasets. Specifically the Dell Data Lakehouse gets:
Native Vector Search Integration in the Dell Data Analytics Engine, powered by Starburst, bringing semantic understanding directly into SQL workflows and bridging the gap between structured query processing and unstructured data exploration.
Hybrid Searchbuilds on the vector search capability by combining semantic similarity with traditional keyword matching, within a single SQL query.
Built-In LLM Functions integrate tools like text summarization and sentiment analysis into SQL-based workflows.
Automated Iceberg Table Management looks after maintenance tasks such as compaction and snapshot expiration.
There are PowerEdge server and network switch updates as part of this overall Dell AI Factory announcement. Dell is announcing Managed Services for the Dell AI Factory with Nvidia to simplify AI operations with management of the full Nvidia AI solutions stack, including AI platforms, infrastructure, and Nvidia AI Enterprise software. Dell managed services experts will handle 24×7 monitoring, reporting, version upgrades, and patching.
The Nvidia AI Enterprise software platform is now available directly from Dell, and customers can use Dell’s AI Factory with Nvidia NIM, NeMo microservices, Blueprints, NeMo Retriever for RAG, and Llama Nemotron reasoning models. They can, Dell says, “seamlessly develop agentic workflows while accelerating time-to-value for AI outcomes.”
Dell AI Factory with Nvidia offerings support the Nvidia Enterprise AI Factory validated design, featuring Dell and Nvidia compute, networking, storage, and Nvidia AI Enterprise software. This provides an end-to-end, fully integrated AI product for enterprises. Red Hat OpenShift is available on the Dell AI Factory with Nvidia.
Availability
Dell Project Lightning is available in private preview for select customers and partners now.
Dell Data Lakehouse updates will be available beginning in July 2025.
Dell ObjectScale with NVIDIA BlueField-3 DPU and Spectrum-4 Ethernet Switches is targeting availability in 2H 2025.
The Dell high-performance system built with Dell PowerScale, Dell Project Lightning and PowerEdge XE servers is targeting availability later this year.
Dell ObjectScale support for S3 over RDMA will be available in 2H 2025.
The NVIDIA AI Enterprise software platform, available directly from Dell, will be available May 2025.
Managed Services for Dell AI Factory with NVIDIA are available now.
Dell is launching an all-flash deduping PowerProtect backup target appliance, providing competition for all-flash systems from Pure Storage (FlashBlade), Quantum, and Infinidat.
The company is also announcing PowerStore enhancements at its Las Vegas-based Dell Technologies World event, plus Dell Private Cloud and NativEdge capabilities.
Arthur Lewis, Dell’s president, Infrastructure Solutions Group, stated: “Our disaggregated infrastructure approach helps customers build secure, efficient modern datacenters that turn data into intelligence and complexity into clarity.”
There are currently four PowerProtect systems plus a software-only virtual edition, offering a range of capacities and speeds.
The All-Flash Ready node is basically a Dell PowerEdge R760 server with 24 x 2.5-inch SAS-4 SSD drives and 8 TB HDDs for cache and metadata, a hybrid flash and disk server. The capacity is up to 220 TB/node and a node supports up to 8 x DS600 disk array enclosures, providing 480 TB raw each, for expanded storage.
The PowerProtect Data Domain All-Flash DD9910F appliance restores data up to four times faster than an equivalent capacity PowerProtect disk-based system, based on testing the DD9910F and an HDD-using DD9910. We don’t get given actual restore speed numbers for any PowerProtect appliance, and Dell isn’t providing the all-flash DD9910F ingest speed either.
All-flash PowerProtect systems have up to twice as fast replication performance and up to 2.8x faster analytics. This is based on internal testing of CyberSense analytics performance to validate data integrity in a PowerProtect Cyber Recovery vault comparing a disk-based DD9910 appliance to the all-flash DD9910F at similar capacity.
They occupy up to 40 percent less rack space and save up to 80 percent on power compared to disk drive-based systems. With Dell having more than 15,000 PowerProtect/Data Domain customers, it has a terrific upsell opportunity here.
PowerStore, the unified file and block storage array, is being given Advanced Ransomware Detection. This validates data integrity and minimizes downtime from ransomware attacks using Index Engines’ CyberSense AI analytics. This indexes data using more than 200 content-based analytic routines. The AI system used has been trained on over 7,000 variants of ransomware and their activity signals. According to Dell, it detects ransomware corruption with a 99.99 percent confidence level.
Dell says it’s PowerStore’s fifth anniversary and there are more than 17,000 PowerStore customers worldwide.
The company now has a Private Cloud to provide a cloud-like environment on premises, built using its disaggregated infrastructure – servers, storage, networking, and software. There is a catalog of validated blueprints. This gives Dell an answer to HPE’s Morpheus private cloud offering.
A Dell Automation Platform provides centralized management and zero-touch onboarding for this private cloud. By using it, customers can provision a private cloud stack cluster in 150 minutes, with up to 90 percent fewer steps than a manual process.
Dell has new NativeEdge products for virtualized workloads at the edge and in remote branch offices. These protect and secure data with policy-based load balancing, VM snapshots and backup and migration capabilities. Customers can manage diverse edge environments consistently with support for non-Dell and legacy infrastructure.
Comment
We expect that Dell announcing its first all-flash PowerProtect appliance will be the trigger ExaGrid needs to bring out its own all-flash product.
The Linus Tech Tips team has calculated Pi, the ratio of a circle’s circumference to its diameter, to a record 300 trillion decimal places with the help of AMD, Micron, Gigabyte, Kioxia, and WEKA.
Linus Sebastian and Jake Tivy of the Canadian YouTube channel produced a video about their Guinness Book of Records-qualified result.
From left, Linus Sebastian and Jake Tivy
The setup involved a cluster of nine Gigabyte storage servers and a single Gigabyte compute node, all running the Ubuntu 22.04.5 LTS operating system. This was because the memory-intensive calculation was done using the Y-cruncher application, which uses external storage as RAM swap space. Eight storage servers were needed to provide the storage capacity needed.
The storage servers were 1 RU Gigabyte R183-Z95-AAD1 systems with 2 x AMD EPYC 9374F CPUs and around 1 TB DDR5 ECC memory. They were fitted with dual ConnectX-6 200 GbE network cards, giving each one 400 GbE bandwidth. Kioxia NVMe CM6 and CD6 series SSDs were used for storage. Overall, there was a total of 2.2 PB of storage spread across 32 x 30.72 TB CM6 SSDs and 80 x 15.36 TB CD6 SSDs for 245 TB per server.
The compute node was a Gigabyte R283-Z96-AAE1 with dual 96-core EPYC 9684X 3D V-Cache CPUs and 196 threads/CPU. There was 3 TB of DRAM, made up from 24 x 128 GB sticks of Micron ECC DDR5 5600MT/s CL46 memory. It was equipped with 4 x Nvidia ConnectX-7 200 GbE network cards with 2 x 200 GbE ports/card and 16 x PCIe Gen 5 bandwidth per slot capable of approximately 64 GBps bidirectional throughput. There was a total of 1.6 Tbps of throughput, around 100 GBps to each 96-core CPU.
WEKA parallel access file system software was used, providing one file system for all nine servers to Y-cruncher. The WEKA software was tricked into thinking each server was two servers so that the most space-efficient data striping algorithm could be used. Each chunk of data was divided into 16 pieces, with two parity segments – 16+2 equaling 18 – matching the 18 virtual nodes achieved by running two WEKA instances per server.
They needed to limit the amount of data that flowed between the CPUs to avoid latency build-ups from one CPU sending data to the other CPU’s network cards and there was also a limited amount of memory bandwidth.
Avoiding cross CPU memory transfers
Tivy configured two 18 GB WEKA client containers – instances of the WEKA application running on the compute node – to access the storage cluster. Each container had 12 cores assigned to it to match the 3D V-Cache of the Zen 4 CPU die. This has 12 chiplets sharing 32 MB of L3 cache with 64 MB layered above for a 96 MB total. Tivy did not want Y-cruncher’s buffers to spill out of that cache because that would mean more memory copies and more memory bandwidth would be needed.
WEKA’s write throughput was around 70.14 GBps and this was over the network with a file system. Read testing showed up to 122.63 GBps with a 2 ms latency. The system was configured with 4 NUMA nodes per CPU, which Y-cruncher leveraged for better memory locality. When reconfigured to a single NUMA node per socket, bandwidth increased increased to 155.17 GBps.
The individual Kioxia CD6 drive read speeds were in excess of 5 GBps. Tivy set the record for the single fastest client usage, according to WEKA. Tivy said WEKA subsequently broke that record with GPUDirect storage and RDMA.
Tivy says Y-cruncher uses the storage like RAM, as RAM swap space. That’s why so much storage is needed. The actual Y-cruncher output of 300 trillion digits is only about 120 TB compressed. Their system used about 1.5 PB of capacity at peak. Y-cruncher is designed for direct-attached storage without hardware or software RAID, as it implements its own internal redundancy mechanisms.
The Linus Tech Tips Pi project started its run on August 1, 2024, and crashed 12 days later due to a multi-day power outage. The run was restarted and then executed past shorter-term power cuts and air-conditioning failures, meaning the calculation had to stop and restart, to complete 191 days later. At that time, they discovered that the 300 trillionth digit of Pi is 5.
Pi calculation history
Here is a brief history of Pi calculation records:
Around 250 BCE: Archimedes approximated π as approximately 22/7 (~3.142857).
5th century CE: Chinese mathematician Zu Chongzhi calculated π to 7 digits (3.1415926), a record that lasted almost 1,000 years.
1596: Ludolph van Ceulen calculated π to 20 digits using Archimedes’ method with polygons of up to 262 sides.
1706: John Machin developed a more efficient arctangent-based formula, calculating π to 100 digits.
1844: Zacharias Dase and Johann Martin Strassmann computed π to 200 digits.
By 1873: William Shanks calculated π to 707 digits. Only the first 527 digits were correct due to a computational error.
By 1949: The ENIAC computer calculated π to 2,037 digits, the beginning of computer-based π calculations.
By 1989: The Chudnovsky brothers calculated π to over 1 billion digits on a supercomputer.
1999: Yasumasa Kanada computed π to 206 billion digits, leveraging the Chudnovsky algorithm and advanced hardware.
2016: 22.4 trillion digits computed by Peter Trueb taking 105.524 days to compute,
2019: Emma Haruka Iwao used Google Cloud to compute π to 31.4 trillion digits (31,415,926,535,897 digits).
2021: Researchers at the University of Applied Sciences of the Grisons, Switzerland, calculated π to 62.8 trillion digits, using a supercomputer, taking 108 days and nine hours.
2022: Google Cloud, again with Iwao, pushed the record to 100 trillion digits, using optimized cloud computing.
This record could surely be exceeded by altering the Linus Tech Tips configuration to one using GPU servers, with an x86 CPU host, GPUs with HBM, and GPUDirect links to faster and direct-attached (Kioxia CM8 series) SSDs. Is a 1 quadrillion decimal place Pi calculation within reach? Could Dell, HPE, or Supermicro step up with a GPU server? Glory awaits.
Nvidia is turning itself into an AI datacenter infrastructure company and storage suppliers are queuing up to integrate their products into its AI software stack. DDN, HPE, and NetApp followed VAST Data in announcing their own integrations at Taiwan’s Computex 2025 conference.
DDN and Nvidia launched a reference design for Nvidia’s AI Data Platform to help businesses feed unstructured data like documents, videos, and chat logs to AI models. Santosh Erram, DDN’s global head of partnerships, declared: “If your data infrastructure isn’t purpose-built for AI, then your AI strategy is already at risk. DDN is where data meets intelligence, and where the future of enterprise AI is being built.”
The reference design combines DDN Infinia with Nvidia’s NIM and NeMo Retriever microservices, RTX PRO 6000 Blackwell Server Edition GPUs, and its networking. Nvidia’s Pat Lee, VP of Enterprise Strategic Partnerships, said: “Together, DDN and Nvidia are building storage systems with accelerated computing, networking, and software to drive AI applications that can automate operations and amplify people’s productivity.” Learn more here.
HPE
Jensen Huang
Nvidia founder and CEO Jensen Huang stated: “Enterprises can build the most advanced Nvidia AI factories with HPE systems to ready their IT infrastructure for the era of generative and agentic AI.”
HPE president and CEO Antonio Neri spoke of a “strong collaboration” with Nvidia as HPE announced server, storage, and cloud optimizations for Nvidia’s AI Enterprise cloud-native offering of NIM, NeMo, and cuOpt microservices, and Llama Nemotron models. The Alletra MP X10000 unstructured data storage array node product will introduce an SDK for Nvidia’s AI Data Platform reference design to offer customers accelerated performance and intelligent pipeline orchestration for agentic AI.
The SDK will support flexible inline data processing, vector indexing, metadata enrichment, and data management. It will also provide remote direct memory access (RDMA) transfers between GPU memory, system memory, and the X10000.
Antonio Neri
HPE ProLiant Compute DL380a Gen12 servers featuring RTX PRO 6000 Blackwell GPUs will be available to order on June 4. HPE OpsRamp cloud-based IT operations management (ITOM) software is expanding its AI infrastructure optimization features to support the upcoming RTX PRO 6000 Blackwell Server Edition GPUs for AI workloads. The OpsRamp integration with Nvidia’s infrastructure including its GPUs, BlueField DPUs, Spectrum-X Ethernet networking, and Base Command Manager will provide granular metrics to monitor the performance and resilience of the HPE-Nvidia AI infrastructure.
HPE’s Private Cloud AI will support the Nvidia Enterprise AI Factory validated design. HPE says it will also support feature branch model updates from Nvidia AI Enterprise, which include AI frameworks, Nvidia NIM microservices for pre-trained models, and SDKs. Support for feature branch models will allow developers to test and validate software features and optimizations for AI workloads.
NetApp
NetApp’s AIPod product – Nvidia GPUs twinned with NetApp ONTAP all-flash storage – now supports the Nvidia AI Data Platform reference design, enabling RAG and agentic AI workloads. The AIPod can run Nvidia NeMo microservices, connecting them to its storage. San Jose-based NetApp has been named as a key Nvidia-Certified Storage partner in the new Enterprise AI Factory validated design.
Nvidia’s Rob Davis, VP of Storage Technology, said: “Agentic AI enables businesses to solve complex problems with superhuman efficiency and accuracy, but only as long as agents and reasoning models have fast access to high-quality data. The Nvidia AI Data Platform reference design and NetApp’s high-powered storage and mature data management capabilities bring AI directly to business data and drive unprecedented productivity.”
NetApp told us “the AIPod Mini is a separate solution that doesn’t incorporate Nvidia technology.”
Availability
HPE Private Cloud AI will add feature branch support for Nvidia AI Enterprise by Summer.
HPE Alletra Storage MP X10000 SDK and direct memory access to Nvidia accelerated computing infrastructure will be available starting Summer 2025.
HPE ProLiant Compute DL380a Gen12 with RTX PRO 6000 Server Edition will be available to order starting June 4, 2025.
HPE OpsRamp Software will be available in time to support RTX PRO 6000 Server Edition.
Bootnote
Dell is making its own AI Factory and Nvidia-related announcements at its Las Vegas-based Dell Technologies World conference. AI agent builder DataRobot announced its inclusion in the Nvidia Enterprise AI Factory validated design for Blackwell infrastructure. DataRobot and Nvidia are collaborating on multiple agentic AI use cases that leverage this validated design. More information here.
HPE expands its storage guarantee program with new SLAs for cyber resilience, zero data loss, and energy efficiency
Today’s enterprise storage customers demand more than just technology; they seek assured IT outcomes. HPE Alletra Storage MP B10000 meets this requirement head-on with an AIOps-powered operational experience and disaggregated architecture that lowers TCO, improves ROI, mitigates risk, and boosts efficiency. We stand behind these promises with industry-leading SLAs and commitments that include free non-disruptive controller upgrades, 100 percent data availability, and 4:1 data reduction.
We’re taking our storage guarantee program to the next level with the introduction of three new B10000 guarantees for cyber resilience, energy efficiency, and zero data loss. These SLA-driven commitments give B10000 customers the confidence and peace of mind to recover quickly from ransomware attacks, lower energy costs with sustainable storage, and experience zero data loss or downtime.
Here’s a breakdown of the new guarantees.
Achieve ransomware peace of mind with cyber resilient storage—guaranteed
Ransomware remains a significant threat to organizations globally, affecting every industry. And attacks are becoming increasingly sophisticated and costly, evolving at an unprecedented pace.
The B10000 offers a comprehensive suite of storage-level ransomware resilience capabilities to help you protect, detect, and rapidly recover from cyberattacks. It features real-time ransomware detection using anomaly detection methodologies to identify malicious encryption indicative of ransomware attacks. In the event of a breach, the B10000 facilitates the restoration of uncorrupted data from tamper-proof immutable snapshots, significantly reducing recovery time, costs, and potential data loss. It also helps identify candidates for data recovery by pinpointing the last good snapshots taken before the onset of an anomaly.
We’re so confident in the built-in ransomware defense capabilities of the B10000 and our professional services expertise that we’re now offering a new cyber resilience guarantee1. This guarantees you access to an HPE services expert within 30 minutes of reporting an outage resulting from a ransomware incident, enabling you to rapidly address and mitigate the impact of a cyberattack. It also ensures that all immutable snapshots created on the B10000 remain accessible for the specified retention period.
HPE’s expert-led outage response services diagnose ransomware alerts and speed up locating and recovering snapshot data. In the unlikely event that we can’t meet the guarantee commitment, we offer you compensation.
Lower your energy costs with sustainable storage—guaranteed
Modern data centers are significant consumers of energy, contributing to 3.5 percent of global CO2 emissions2, with 11 percent of data center power used for storage3. As data grows exponentially, so does the environmental and cost impact. Transitioning from power-hungry, hardware-heavy legacy infrastructure to modern, sustainable storage is no longer optional; it’s now a fundamental business imperative.
The B10000 features a power-efficient architecture that enhances performance, reliability, and simplicity while helping reduce energy costs, carbon emissions, and e-waste compared to traditional storage. With the B10000, you can reduce your energy consumption by 45 percent4, decrease your storage footprint by 30 percent5, and lower your total cost of ownership by 40 percent6.
The B10000 achieves these impressive sustainability outcomes with energy-efficient all-flash components, disaggregated storage that extends product life cycles while reducing e-waste, advanced data reduction capabilities, and AI-powered management that optimizes resource usage. Better still, B10000 management tools on the HPE GreenLake cloud like the HPE Sustainability Insight Center and the Data Services Cloud Console provide visibility into energy consumption and emissions, enabling informed decision-making to meet sustainability goals.
Because the B10000 is designed for sustainability, performance, and efficiency, we can guarantee that it operates at optimized power levels while maintaining peak performance. Our new energy consumption guarantee7 promises that your B10000 power usage will not exceed an agreed maximum target each month. This helps ensure that you can plan for and count on a maximum power budget. If you exceed the energy usage limit, you will receive a credit voucher to offset your additional energy costs.
Sleep better at night with zero data loss or downtime—guaranteed
Application uptime is more important today than it’s ever been. Data loss and downtime mean lost time and money. You need highly available storage that will ensure multi-site business continuity and data availability for your mission-critical applications in the event of unexpected disruptions.
That’s why we are introducing a new HPE Zero RTO / RPO guarantee for the B100008. It’s a cost-nothing, do-nothing guarantee that is unmatched in the industry. The active peer persistence feature of the B10000 combines synchronous replication and transparent fail-over across active sites to maintain continuous data availability with no data loss or downtime—even in the event of site-wide or natural disasters. Because active peer persistence is built into the B10000, there are no additional appliances or integration required.
If your application loses access to data during a fail-over, we will proactively provide credit that can be redeemed upon making a future investment in the B10000.
Simplify and future-proof your storage ownership experience
In today’s fast-moving business world, your data infrastructure needs to keep up without surprises, disruptions, or unexpected costs. HPE Alletra Storage MP B10000 simplifies and future-proofs your experience, offering guaranteed outcomes, investment protection, and storage that gets better over time.
With the B10000, you get best-in-class ownership experience on the industry’s best storage array. And our outcome-focused SLAs provide peace of mind, so you can focus on business objectives rather than IT operations. Your storage stays modern, your costs stay predictable, and your business stays ahead.
The cyber resilience guarantee is available on any B10000 system running OS release 10.5. The guarantee is applicable whether you purchase your B10000 system through a traditional up-front payment or through the HPE GreenLake Flex pay-per-use consumption model. To be eligible for the guarantee, you will need to have an active support contract with a minimum three-year software and support software-as-a-service (SaaS) subscription.
“Top Recommendations for Sustainable Data Storage,” Gartner, November 2024.
“Top Recommendations for Sustainable Data Storage,” Gartner, November 2024.
Based on internal analysis comparing power consumption, cooling requirements, and energy efficiency when transitioning from spinning disk to HPE Alletra Storage MP all-flash architecture.
Based on internal use case analysis, HPE Alletra Storage MP B10000 reduces total cost of ownership (TCO) versus non-disaggregated architectures by allowing organizations to avoid overprovisioning.
Unlike competitive energy efficiency SLAs, the B10000 guarantee is applicable whether you purchase your B10000 system through a traditional up-front payment or the HPE GreenLake Flex pay-per-use consumption model. To qualify for this guarantee, an active HPE Tech Care Service or HPE Complete Care Service contract is required.
To qualify for the HPE Zero RTO/RPO guarantee, customers must have an active support contract, identical array configurations in the replication group, and a minimum three-year SaaS subscription.
VAST Data is integrating Nvidia’s AI-Q AI agent building and connecting blueprints into its storage to help its customers enter the AI agent-building rat race.
AI-Q is a blueprint or reference design plan for integrating Nvidia GPUs, partner storage platforms, and software and Agent Intelligence Toolkit when developing AI agents. It includes Nvidia’s Llama Nemotron reasoning models, NeMO retriever and NIM microservices. The Agent Intelligence Toolkit, available on GitHub, is an open-source software library for connecting, profiling and optimizing teams of AI agents. It integrates with frameworks and tools like CrewAI, LangGraph, Llama Stack, Microsoft Azure AI Agent Service and Letta. A storage partner, such as VAST, continually processes data so that connected agents can respond to data changes, reasoning and acting on them.
Jeff Denworth.
Jeff Denworth, Co-Founder at VAST Data, stated: “The agentic era is going to challenge every assumption about the scale, performance, and value of legacy infrastructure. As enterprises race to operationalize AI, it’s no longer enough to just build smarter models — those models need immediate, unrestricted access to the data that drives intelligent decision-making. By embedding NVIDIA AI-Q into a platform purpose-built for the AI era, we’re delivering the scalable, high-performance data platform required to power the next generation of enterprise AI; one driven by real-time, multimodal intelligence, continuous learning, and dynamic agentic pipelines.”
VAST Data says the AI-Q blueprint “povides an environment for rapid metadata extraction and establishes heterogeneous connectivity between agents, tools, and data, simplifying the creation and operationalization of agentic AI query engines that reason across structured and unstructured data with transparency and traceability.”
Within the AI-Q environment, the VAST Data storage and AI engine component data stack is a secure, AI-native pipeline that takes raw data and transforms and feeds it upstream to AI Agents. As part of that Nemo Retriever is used by VAST and AI-Q to extract, embed, and rerank relevant data before passing it to advanced language and reasoning models.
Nvidia AI-Q diagram.
VAST plus AI-Q will provide:
Multi-modal unstructured and semi-structured data RAG including enterprise documents, images, videos, chat logs, PDFs, and external sources like websites, blogs, and market data.
Structured data with VAST connecting AI agents directly to structured data sources such as ERP, CRM, and data warehouses for real-time access to operational records, business metrics, and transactional systems.
Fine-grained user and agent access control through policies and security features.
Real-time agent optimization via Nvidia’s Agent Intelligence Toolkit and VAST’s telemetry.
Justin Boitano.
VAST says it and Nvidia are enabling organizations to build real-time AI intelligence engines to enable teams of AI agents to deliver more accurate responses, automate multi-step tasks, and continuously improve via an AI data flywheel.
Justin Boitano, VP, Enterprise AI at Nvidia, said: “AI-driven data platforms are key to helping enterprises put their data to work to drive sophisticated agentic AI systems. Together, NVIDIA and VAST are creating the next generation of AI infrastructure with powerful AI systems that let enterprises quickly find insights and knowledge stored in their business data.”
VAST’s Jon Mao, VP of Business Development and Alliances, blogs “By leveraging the Nvidia AI-Q blueprint and a powerful suite of Nvidia software technologies — from Nvidia NeMo and Nvidia NIM microservices to Nvidia Dynamo, Nvidia Agent Intelligence toolkit, and more — alongside the VAST InsightEngine and our VUA acceleration layer, this platform empowers enterprises to deploy AI agent systems capable of reasoning over enterprise data, to deliver faster, smarter outcomes. It’s a new kind of enterprise AI stack, built for the era of AI agents — and VAST is proud to be leading the way.”
Read more in Mao’s blog.
Comment
Until recently a storage system only needed to support GPUDirect to deliver data fast to Nvidia’s GPUs. Now it needs to integrate with the Nvidia AI-Q blueprint and continually feed data to Nvidia agents and AI software stack components, which use the GPUs, so as to become an Nvidia AI data platform. We predict more storage suppliers will adopt AI-Q
Western Digital has extended its platform chassis business at Computex with a new disk drive box, NVMe SSD box, qualified partner SSDs, and an extended compatibility lab.
The company, before the SanDisk split, had a storage systems business, which included IntelliFlash arrays, ActiveScale object storage systems, the Kazan Networks PCIe-to-NVMe bridge business, and Ultrastar JBODS and OpenFlex JBOFs. This business was ultimately discontinued, with some components sold off – IntelliFlash to DDN in 2019 and ActiveScale to Quantum in 2020. That left it with the Ultrastar and OpenFlex chassis, and RapidFlex PCIe-NVMe bridge technologies grouped under a platforms banner. It spun off the Sandisk NAND fab and SSD business earlier this year, leaving its revenues overwhelmingly dominated by disk drive sales, yet it retained the SSD side of the platforms business and is now emphasizing its commitment with this set of releases.
Western Digital is announcing a new Ultrastar Data102 JBOD that is OCP ORv3 compliant, the OpenFlex Data24 4100 Ethernet-connected JBOF (EBOF) with single-port SSDs, which complements the existing 4200 with its dual-port SSD, new SSD qualifications for the OpenFlex chassis, and new capabilities in Western Digital’s Open Composable Compatibility Lab (OCCL).
Kurt Chan
Kurt Chan, VP and GM of Western Digital’s Platforms Business, stated: “With OCCL 2.0 and our latest Platform innovations, we’re not just keeping up – we’re setting the pace for what modern, disaggregated, and software-defined datacenters can achieve. We remain deeply committed to enabling open, flexible architectures that empower customers to build scalable infrastructure tailored to their evolving data needs.”
The Ultrastar Data102 3000 ORv3 JBODis designed to meet the Open Rack v3 (ORv3) specification, which focuses on rack design and power supply regulation. It complies with Rack Geometry Option 1, uses building blocks from the Data102 3000 series such as controllers, enclosures, and Customer Replaceable Units (CRUs), and is compliant with FIPS 140-3 Level 3 and TAA standards.
This chassis features up to 2.44 PB of raw capacity in a 4RU enclosure with 102 drive slots. There can be dual-port SAS for high availability or single-port SATA for cloud-like applications and economics, and up to 12 x 24 Gbps SAS-4 host connections.
Ultrastar Data102 3000
The OpenFlex Data24 4100 EBOF is designed for cloud-like environments where high availability is not a primary requirement. It uses single-port SSDs, up to 24 of them, where redundancy is provided by mirroring the storage system.
Western Digital says it has qualified SSDs from DapuStor, Kioxia, Phison, Sandisk and ScaleFlux with more vendors in the qualification process.
OpenFlex Data24 4100
The Western Digital Open Composable Compatibility Lab (OCCL) was set up in August 2019, Colorado Springs, to encourage support for open, composable, disaggregated infrastructure (CDI). It provided interoperability testing with partners like Broadcom and Mellanox for NVMe-over-Fabrics (NVMe-oF) systems, like its OpenFlex components, and therefore involved SSDs.
It is now launching OCCL v2.0, saying it will provide testing across disaggregated compute, storage and networking for NVMe-oF architecture systems – still having an SSD focus unless NVMe-accessible disk drives are in development – and components featuring:
Detailed solution architectures with guidance on deploying and managing composable disaggregated infrastructure effectively.
Disaggregated storage best practices.
Industry Expertise sharing strategic insights and innovations in the field of composable infrastructure.
SSD partner benchmarking with comprehensive results from benchmarking tools to evaluate the performance of SSD partners, ensuring optimal storage solutions.
The Data24 4100 is expected to be available in calendar Q3 2025 with the Data102 3000 expected in calendar Q4 2025.
Visit Western Digital at Computex in Taipei, at booth J1303a in Hall 1 Storage and Management Solutions at the Taipei Nangang Exhibition Center from May 20-23.
Analysis.Cohesity is moving beyond data protection and cyber-resilience by building a real-time data access and management facility alongside its existing data protection access pipelines so that it can bring information from both live and backed-up data to GenAI models.
Greg Statton
This recognition came from a discussion with Cohesity’s VP for AI Solutions, Greg Statton, that looked at metadata and its use in AI data pipelining.
An AI model needs access to an organization’s own data so that it can be used for retrieval-augmented generation, thereby producing responses pertinent to the organization’s staff and based on accurate and relevant data. Where does this data come from?
An enterprise or public sector organization will have a set of databases holding its structured and also some unstructured information, plus files and object data. All this data may be stored in on-premises systems – block, file, or object, unified or separate – and/or in various public cloud storage instances. AI pipelines will have to be built to look at these stores, filter and extract the right data, vectorize the unstructured stuff, and feed it all to the AI models.
This concept is now well understood and simple enough to diagram:
All diagrams are Blocks & Files creations
This diagram shows a single AI pipeline, but that is a simplification as there could be several being fed from different data resources, such as ERM applications, data warehouses, data lakes, Salesforce and its ilk, and so forth. But bear with us as we illustrate our thinking with a single pipeline.
We’re calling this data live data, as it is real-time, and as a way of distinguishing it from backup data. But, of course, there are vast troves of data in backup stores and an organization’s AI Models get another view of its data estate which they can mine for user request responses. Data protection suppliers, such as Cohesity, Commvault, Rubrik, and Veeam. All four, and others, are building what we could call backup-based AI pipelines. Again, this can be easily diagrammed:
We now have two different AI data pipelines, one for live data and one for backup data. But that’s not all; there is also archival data, stored in separate repositories from the live and backup data. We can now envisage a third archival data AI pipeline is needed and, again, it is simple to diagram:
We are now at the point of having three separate AI model data pipelines – one each for live data, backup data, and archival data:
Ideally, we need all three, as together they provide access to the totality of an organization’s data.
This is wonderful but it comes with a considerable disadvantage. Although it gives the AI models access to all of an organization’s data, it is inefficient, will take some considerable time to build and also effort to maintain. As on-premises data is distributed between data centers and edge locations, and also the public cloud, and between structured and unstructured data stores, with the environment being dynamic and not static, ongoing maintenance and development will be extensive and necessary. This is going to be costly.
What we need is a universal data access layer, with touch points for live, backup and archive data, be it on-premises and distributed across sites and applications, in the public cloud as storage instances, or in SaaS applications, or a hybrid of these three.
Cohesity’s software already has touch points (connectors) for live data. It has to in order to back it up. It already stores metadata about its backups and archives. It already has its own AI capability, Gaia, to use this metadata, and also to generate more metadata about a backup item’s (database, records, files, objects, etc.) context and contents and usage. It can vectorize these items and locate them in vector spaces according to their presence or absence in projects for example.
Let’s now picture the situation as Cohesity sees it, in my understanding, with a single and universal access layer:
Cohesity can become the lens through which AI models look at the entirety of an organization’s data estate to generate responses to user questions and requests. This is an extraordinarily powerful yet simple idea. How can this not be a good thing? If an AI data pipeline function for an organization can not cover all three data types – live, backup, and archive – then it is inherently limited and less effective.
It seems to Blocks & Files that all the data protection vendors looking to make their backups data targets for AI models will recognize this, and want to extend their AI data pipeline functionality to cover live data and also archival stores. Cohesity has potentially had a head start here.
Another angle to consider – the live data-based AI pipeline providers will not be able to extend their pipelines to cover backup, also archive, data stores unless they have API access to those stores. Such APIs are proprietary and negotiated partnerships will be needed but may not be available. It’s going to be an interesting time as the various vendors with AI data pipelines wrestle with the concept of universal data access and what it means for their customers and the future of their own businesses.
An analyst report says disk drive capacity shipments are orojected to rise steeply bupwards between now and 2030.
Tom Coughlin
This data is included in Tom Coughlin’s latest 174-page Digital Storage Technology Newsletter which looks at how the three disk drive suppliers fared in the first 2025 quarter and forecasts disk drive units abd capacity shipped out to 2030.
Leader Western Digital gained a point of market share from Toshiba in the first 2025 quarter as disk drive units and exabytes shipped both dipped relative to the final 2024 quarter.
There were 29.7 million drive shipped in the first 2025 quarter, about 9.5 percent down on the prior quarter’s 31.7 million, with mass-capacity nearline drive ships reducing 12 percent. The exabytes shipped total of 361.4 EB was down 4.4 percent over the same period. Total HDD supplier revenues were down around 8.4 percent quarter-on-quarter (Q/Q) to $5.242 billion.
Western Digital delivered 12.1 million drives and 179.8EB with $2.294 billion revenues and 42 percent market share by units.
Seagate shipped 11.4 million drives and 143.6 EB earning $2.003 billion with a 40 percent market share.
Toshiba shipped 5.2 million drives and 41 EB with an 18 percent market share and $945 million in revenues.
The drive format unit ship categories fared differently in a quarter-on-quarter (Q/Q) basis:
Notebook: up 1.4 percent
Desktop: up 1.4 percent
Consumer Electronics: down 10.4 percent
Branded: down 11.3 percent
High-performance enterprise: down 15.2 percent
Nearline enterprise: down 11.3 percent
Overall 3.5-inch drive unit shipped went down 8.7 percent to 22.7 million while 2.5-inch units dipped 12.9 percent to 6 million. This all reflects the increasing industry concentration on building 3.5-inch nearline drives.
Coughlin says there was a a 4.9 percent average sales price (ASP) increase Q/Q in Q1 2025 and presented a ASP history chart, based on Seagate and WD ASPs, showing a near-symmetrical bathtub curve:
He notes: “HDD ASPs are now at levels higher than seen at the end of 1998 due to the increased demand for high capacity, high ASP, HDDs and the increase in the percentage of these drives shipped.”
The report includes a chart showing high, media and low estimates for HDD unit ships out to 2030, with all three rising:
He reckons disk drive areal density has been increasing at 20 percent annually since the year 2000, with a 40-40 percent CAGR from 2000 to around 2010, and then a flattening off to something like 10 percent CAGR from then to 2024.
Coughlin’s report has a disk drive annual exabytes shipped projection chart showing a steep rise from 2024 to 2030:
We don’t know if this is based on the high, medium or low HDD unit ship projections and have asked. Tom Coughlin replied: “Yes, these two [charts] are tied together. The capacity shipment projection reflects my projections for average storage capacity of the HDDs and the median shipment projection.”
Altogether, beyond the ongoing steady attrition of notebook, PC and high-performance disk drives by SSDs, there is no sign of SSDs replacing nearline, mass-capacity HDDs in any significant way at all.
Open source ETL connector biz Airbyte announced progress in the first quarter with revenues up 25 percent, industry recognition, new product features, and Hackathon results. Michel Tricot, co-founder and CEO, Airbyte, said: “This year is off to a great start on every level – customer acceptance and revenue growth, industry recognition, and product development as we continue to evolve our platform to offer the very best technology to our users. There is much more to do and we’re working hard on the next wave of technology that includes the ability to move data across on-premises, cloud, and multiple regions which is all managed under one control plane.” With over 900 contributors and a community of more than 230,000 members, Airbyte supports the largest data engineering community and is the industry’s only open data movement platform.
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The UK’s Archive360 says it has released the first modern archive platform that provides governed data for AI and analytics. It’s a secure, scalable data platform that ingests data from all enterprise applications, modern communications, and legacy ERP into a data-agnostic, compliant active archive that feeds AI and analytics. It is deployed as a cloud-native, class-based architecture. It provides each customer with a dedicated SaaS environment to enable them to completely segregate data and retain administrative access, entitlements, and the ability to integrate into their security protocols.
Support for enterprise databases including SAP, Oracle, and SQL Server enables streamlined ingestion and governance of structured data alongside unstructured content to provide a unified view across the organization’s data landscape. There are built-in connectors to leading analytics and AI platforms such as Snowflake, Power BI, and OpenAI. Find out more here.
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Box CEO Aaron Levie posted an X tweet: “Box announced … new capabilities to support AI Agents that can do Deep Research, Search, and enhanced Data Extraction on your enterprise content, securely, in Box. And all with a focus on openness and interoperability. Imagine being able to have AI Agents that can comb through any amount of your unstructured data – contracts, research documents, marketing assets, film scripts, financial documents, invoices, and more – to produce insights or automate work. Box AI Agents will enable enterprises to automate a due diligence process on hundreds or thousands of documents in an M&A transaction, correlate customer trends amongst customer surveys and product research data, or analyze life sciences and medical research documents to generate reports on new drug discovery and development.”
“AI Agents from Box could work across an enterprise’s entire AI stack, like Salesforce Agentforce, Google Agentspace, ServiceNow AI Agent Fabric, IBM watsonx, Microsoft Copilot, or eventually ChatGPT, Grok, Perplexity, Claude, and any other product that leverages MCP or the A2A protocol. So instead of moving your data around between each platform, you can just work where you want and have the agents coordinate together in the background to get the data you need. This is the future of software in an era of AI. These new Box AI Agent capabilities will be rolling out in the coming weeks and months to select design partner customers, and then expand to be generally available from there.”
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Datalaker Databricks intends to acquire serverless Postgres business Neon to strengthen Databricks’ ability to serve agentic and AI-native app developers. Recent internal telemetry showed that over 80 percent of the databases provisioned on Neon were created automatically by AI agents rather than by humans. Ali Ghodsi, Co-Founder and CEO at Databricks, said: “By bringing Neon into Databricks, we’re giving developers a serverless Postgres that can keep up with agentic speed, pay-as-you-go economics and the openness of the Postgres community.”
Databricks says the integration of Neon’s serverless Postgres architecture with its Data Intelligence Platform will help developers and enterprise teams build and deploy AI agent systems. This approach will prevent performance bottlenecks from thousands of concurrent agents and simplify infrastructure while reducing costs. Databricks will continue developing Neon’s database and developer experience. Neon’s team is expected to join Databricks after the transaction closes.
Neon was founded in 2021 and has raised $130 million in seed and VC funding with Databricks investing in its 2023 B-round. The purchase price is thought to be $1 billion. Databricks itself, which has now acquired 13 other businesses, has raised more than $19 billion; there was a $10 billion J-round last year and $5 billion debt financing earlier this year.
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Databricks is launching Data Intelligence for Marketing, a unified data and AI foundation already used by global brands like PetSmart, HP, and Skechers to run hyper-personalized campaigns. The platform gives marketers self-serve access to real-time insights (no engineers required) and sits beneath the rest of the martech stack to unlock the value of tools like Adobe, Salesforce and Braze. Early results include a 324 percent jump in CTRs and 28 percent boost in return on ad spend at Skechers, and over four billion personalized emails powered annually for PetSmart. Databricks aims to become the underlying data layer for modern marketing and bring AI closer to the people who actually run campaigns.
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Data integration supplier Fivetran released its 2025 AI & Data Readiness Research Report. It says AI plans look good on paper but fail in practice, AI delays are draining revenue, and centralization lays the foundation, but it can’t solve the pipeline problem on its own – data readiness is what unlocks AI’s full potential. Get the report here.
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Monica Ohara
Fivetran announced Monica Ohara as its new Chief Marketing Officer. Ohara brings a wealth of expertise, having served as VP of Global Marketing at Shopify and led rider and driver hyper-growth through the Lyft IPO.
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In-memory computing supplier GridGain released its GridGain Platform 9.1 software with enhanced support for real-time hybrid analytical and transactional processing. The software combines enhanced column-based processing (OLAP) and row-based processing (OLTP) in a unified architecture. It says v9.1 is able to execute analytical workloads simultaneously with ultra-low latency transactional processing, as well as feature extraction and the generation of vector embeddings from transactions, to optimize AI and retrieval-augmented generation (RAG) applications in real time. v9.1 has scalable full ACID compliance plus configurable strict consistency. The platform serves as a feature or vector store, enabling real-time feature extraction or vector embedding generation from streaming or transactional data. It can act as a predictions cache, serving pre-computed predictions or running predictive models on demand, and integrates with open source tools and libraries, such as LangChain and Langflow, as well as commonly used language models. Read more in a blog.
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A company called iManage provides cloud-enabled document and email management for professionals in legal, accounting, and financial services. It has announced the early access availability of HYCU R-Cloud for iManage Cloud, an enterprise-grade backup and recovery solution purpose-built for iManage Cloud customers. It allows customers to maintain secure, off-site backups of their iManage Cloud data in customer-owned and managed storage, supporting internal policies and regional requirements for data handling and disaster recovery, and is available on the HYCU marketplace. Interested organizations should contact their iManage rep to learn more or apply for participation in the Early Access Program.
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We have been alerted to a 39-slide IBM Storage Ceph Object presentation describing the software and discussing new features in v8. Download it here.
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IBM Storage Scale now has a native REST API for its remote and secure admin. You can manage the Storage Scale cluster through a new daemon that runs on each node. This feature replaces the administrative operations that were previously done with mm-commands. It also eliminates several limitations of the mm-command design, including the dependency on SSH, the requirement for privileged users, and the need to issue commands locally on the node where IBM Storage Scale runs. The native REST API includes RBAC, allowing the security administrator to grant granular permissions for a user. Find out more here.
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Data integration and management supplier Informatica announced expanded partnerships with Salesforce, Nvidia, Oracle, Microsoft, AWS, and Databricks at Informatica World:
Microsoft: New strategic agreement continuing innovation and customer adoption on the Microsoft Azure cloud platform, and deeper product integrations.
AWS:Informatica achieved AWS GenAI Competency, along with new product capabilities including AI agents with Amazon Bedrock, SQL ELT for Amazon Redshift and new connector for Amazon SageMaker Lakehouse.
Oracle: Informatica’s MDM SaaS solution will be available on OracleCloud Infrastructure, enabling customers to use Informatica MDM natively in the OCI environment.
Databricks:Expanded partnership to help customers migrate to its AI-powered cloud data management platform to provide a complete data foundation for future analytics and AI workloads.
Salesforce:Salesforce will integrate its Agentforce Platform with Informatica’s Intelligent Data Management Cloud, including its CLAIRE MDM agent.
Nvidia: Integration of Informatica’s Intelligent Data Management Cloud platform with Nvidia AI Enterprise. Nvidia AI Enterprise will deliver a seamless pathway for building production-grade AI agents leveraging Nvidia’s extensive optimized and industry-specific inferencing model.
Informatica is building on its AI capabilities (CLAIRE GPT, CLAIRE Copilot), with its latest agentic offerings, CLAIRE Agents and AI Agent Engineering.
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Kingston has announced its FURY Renegade G5, PCIe Gen 5×4, NVMe, M.2 2280 SSD with speeds up to 14.8/14.0 GBps sequential read/write bandwidth and 2.2 million random read and write IOPS for gaming and users needing high performance. It uses TLC 3D NAND. It has a Silicon Motion SM2508 controller based on 6nm lithography and low-power DDR4 DRAM cache. The drive is available in 1,024 GB, 2,048 GB and 4,096 GB capacities, backed by a limited five-year warranty and free technical support. The endurance is 1PB per TB of capacity.
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Micron announced that its LPDDR5X memory and UFS 4.0 storage are used in Motorola’s latest flip phone, the Razr 60 Ultra, which features Moto AI “delivering unparalleled performance and efficiency.” Micron’s LPDDR5X memory, based on its 1-beta process, delivers speeds up to 9.6 Gbps, 10 percent faster than the previous generation. LPDDR5X offers 25 percent greater power savings to fuel AI-intensive applications and extend battery life – a key feature for the Motorola Razr 60 Ultra with over 36 hours of power. Micron UFS 4.0 storage provides plenty of storage for flagship smartphones to store the large data sets for AI applications directly on the device, instead of the cloud, enhancing privacy and security.
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Data protector NAKIVO achieved a 14 percent year-on-year increase in overall revenue, grew its Managed Service Provider (MSP) partner network by 31 percent, and expanded its global customer base by 10 percent in its Q1 2025 period. NAKIVO’s Managed Service Provider Programme empowers MSPs, cloud providers, and hosting companies to offer services such as Backup as a Service (BaaS), Replication as a Service (RaaS), and Disaster Recovery as a Service (DRaaS). NAKIVO now supports over 16,000 active customers in 185 countries. The customer base grew 13 percent in the Asia-Pacific region, 10 percent in EMEA, and 8 percent in the Americas. New NAKIVO Backup & Replication deployment grew by 8 percent in Q1 2025 vs Q1 2024.
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Dedicated Ootbi Veeam backup target appliance builder Object First announced a 167 percent year-over-year increase in bookings for Q1 2025. Its frenetic growth is slowing as it registered 294 percent in Q4 2024, 347 percent in Q3 2024, 600 percent in Q2 2024 and 822 percent in Q1 2024. In 2025’s Q1 there was year-over-year bookings growth of 92 percent in the U.S. and Canada, and 835 percent in EMEA, plus growth of 67 percent in transacting partners and 185 percent in transacting customers.
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Europe’s OVHcloud was named the 2025 Global Service Provider of the Year at Nutanix‘s annual .NEXT conference in Washington, D.C. It was recognized for its all-in-one and scalable Nutanix on OVHcloud hyper-converged infrastructure (HCI), which helps companies launch and scale Nutanix Cloud Platform (NCP) software licenses on dedicated, Nutanix-qualified OVHcloud Hosted Private Cloud infrastructure.
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Objective Analysis consultant Jim Handy has written about Phison’s aiDAPTIV+SSD hardware and software. He says Phison’s design uses specialized SSDs to reduce the amount of HBM DRAM required in an LLM training system. If a GPU is limited by its memory size, it can provide greatly improved performance if data from a terabyte SSD is effectively used to swap data into and out of the GPU’s HBM. Handy notes: “This is the basic principle behind any conventional processor’s virtual memory system as well as all of its caches, whether the blazing fast SRAM caches in the processor chip itself, or much slower SSD caches in front of an HDD. In all of these implementations, a slower large memory or storage device holds a large quantity of data that is automatically swapped into and out of a smaller, faster memory or storage device to achieve nearly the same performance as the system would get if the faster device were as large as the slower device.” Read his article here.
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Private cloud supplier Platform9 has written an open letter to VMware customers offering itself as a target for switchers. It includes this text: “Broadcom assured everyone that they could continue to rely on their existing perpetual licenses. Broadcom’s message a year ago assured us all that ‘nothing about the transition to subscription pricing affects our customers’ ability to use their existing perpetual licenses.’ This past week, that promise was broken. Many of you have reported receiving cease-and-desist orders from Broadcom regarding your use of perpetual VMware licenses.” Read the letter here.
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Storage array supplier StorONE announced the successful deployment of its ONE Enterprise Storage Platform at NCS Credit, the leading provider of notice, mechanic’s lien, UCC filing, and secured collection services in the US and Canada. StorONE is delivering performance, cost efficiency, and scalability to NCS to help them in securing the company’s receivables and reducing its risk. Its technology allowed NCS to efficiently manage workloads across 100 VMs, databases, and additional applications, with precise workload segregation and performance control.
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Western Digital’s board has authorized a new $2 billion share repurchase program effective immediately.
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Western Digital has moved its 26 TB Ultrastar DC H590 11-platter, 7,200 rpm, ePMR HDD technology, announced in October last year, into its Red Pro NAS and Purple Pro surveillance disk product lines. The Red Pro disk is designed for 24/7 multi-user environments, has an up to 272 MBps transfer speed, rated for up to 550 TB/year workloads and has a 2.5 million hours MTBF. The Purple Pro 26 TB drive is for 24×7 video recording, the same workload rating, and has AllFrameAI technology that supports up to 64 single-stream HD cameras or 32 AI streams for deep learning analytics. The 26 TB Red Pro and Purple Pro drives cost $569.99 (MSRP).
