Microsoft has launched Microsoft 365 Backup and Microsoft 365 Backup Storage, introducing cloud data backup directly on its platform, which is also accessible for third-party use.
Several have jumped aboard at once – AvePoint, Cohesity, Commvault, Keepit, Rubrik, Veeam, and Veritas – and will be keeping all or some of their Microsoft 365 customer backup data in Azure. Microsoft software engineers have probably done clever things with snapshots and metadata to speed things up.
Microsoft diagram
Microsoft 365 Backup provides backup and recovery for active data to maintain business continuity “at speeds orders of magnitude faster than with traditional migration-based backup methodologies.” It has “ultra-high-speed recovery of your OneDrive, SharePoint, and Exchange data.” Microsoft says: “Many customers will see average speeds for mass restores that are 20 times faster than traditional means of backing up and restoring large volumes of Microsoft 365 data.”
We haven’t seen any benchmark test numbers and Microsoft bases its claim on internal research. However, if a service like Exchange Online or Teams is based in Azure, then backing it up within Azure is intuitively going to be faster than copying the data outside Azure.
Microsoft 365 Backup Storage powers the Microsoft 365 Backup offering. It gives third-party backup developers, using the Microsoft 365 Backup Storage APIs, a way to provide the same features of Microsoft 365 Backup through their own integrated application.
Commvault
Commvault Cloud is built on Azure, and Commvault has announced Microsoft 365 Backup Storage as an integrated component of Commvault Cloud Backup and Recovery for Microsoft 365. It delivers cyber resilience and recovery across Microsoft 365 workloads. Native integration provides unified control with single-pane-of-glass monitoring and administration via Commvault Cloud.
Commvault’s Microsoft 365 protection capabilities across Exchange Online, Teams, OneDrive, and SharePoint include selectable and configurable extended retention, granular recovery, and self-restore options.
Tirthankar Chatterjee
Tirthankar Chatterjee, CTO Hyperscalers at Commvault, stated: “We are building on our 27-plus years of co-development and co-engineering with this integrated solution that helps customers achieve the highest level of cyber resilience and threat readiness, along with the ability to greatly improve recovery time to specific Microsoft 365 workloads.”
Zach Rosenfield, Microsoft’s Partner Director of PM, Collaborative Apps and Platforms, stated: “With this integration, Commvault has taken a major step in helping our joint customers keep their business running with fast operational recovery from cyberattacks.”
Commvault Cloud Backup and Recovery for Microsoft 365 will be available through the Microsoft Azure Marketplace later this quarter.
Keepit
SaaS backup provider Keepit is, on the face of it, an unlikely Microsoft Backup Storage partner. The Keepit service offers fast, immutable, independent, always-online storage that protects Microsoft 365, Entra ID, Google Workspace, Salesforce, and more. It stores the backup data in its own privately operated datacenters in the USA, Canada, UK, Germany, Denmark, and Australia.
Paul Robichaux
Keepit’s Paul Robichaux, Senior Director of Product Management, writes in a blog: “We are adding support for Microsoft 365 Backup Storage so that you can flexibly choose to add rapid-restore protection for your most critical Microsoft 365 assets.”
“Microsoft has invested decades of engineering experience and knowledge into the Microsoft 365, Entra ID, Power Platform, and Dynamics 365 platforms. Because they have complete control over and visibility into every aspect of those platforms, their first-party backup solution delivers some great technical capabilities, including high restore speeds at large scale and great data fidelity. The combination of database level backup for SharePoint and OneDrive and copy-on-write backup for Exchange Online gives customers a powerful new tool for large-scale recovery. No other vendor can provide the same direct capabilities because none of us are “inside the blue curtain.” We just do not have the same access to the platform that Microsoft does.”
While Microsoft has unique access to their platform, what Keepit says it contributes is unique separation of environments and guaranteed access to data: “With data stored in the independent Keepit cloud, customers retain access even if they lose access to Microsoft.”
He says that the SaaS backup discussion is changing with the increased recognition that the customer is responsible for ensuring the security and availability of their own SaaS control plane and application data in a world that is increasingly insecure. The SaaS vendor can do a lot to help, but ultimately it’s the customer’s responsibility: “They ignore that at their peril.”
“Keepit protects the full range of critical objects, including conditional access policies, application registrations, users, mailboxes, SharePoint sites, Teams channels, Microsoft 365 Groups, CRM data, and more; Microsoft 365 Backup Storage adds rapid-restore protection for the object types they protect.”
“For customers, the perfect backup setup for Microsoft 365 will be this: A full, immutable, logically and physically separate backup of all of Microsoft 365 and Entra ID in Keepit, with extra restore capabilities of critical data sets in Microsoft 365 Backup Storage. In this setup, customers can keep costs under control, and have guaranteed access to all data, in the event of losing access to Microsoft tenants or administrator credentials.”
Keepit’s integration with Microsoft 365 Backup Storage is currently in private preview and it’s rolling this feature out to eligible customers soon. The Keepit Microsoft 365 roadmap includes:
Keepit will suggest which data items might benefit from rapid restore protection, using data about activity and cost to intelligently balance recovery time, coverage, and cost
When a user requests a restore, Keepit will know exactly where to retrieve the data from to get it back both completely and quickly
Seamless integrated restore across both storage platforms, allowing one-click restore of Entra ID alongside Microsoft 365 data
Automatic migration of data between platforms to provide cost-effective long-term data preservation giving you the right protection for the different types of data you have at the right cost
Integrated auditing and management to help define, monitor, and enforce backup and compliance policie
Veeam
Veeam Data Cloud is built on Azure and provides backup-as-a-service (BaaS) for Microsoft 365, with more than 21 million users protected. The company says it’s a launch partner for Microsoft 365 Backup Storage and Veeam Data Cloud for Microsoft 365 uses Microsoft’s 365 Backup Storage offering. This new Microsoft backup technology is embedded inside Veeam’s backup service for Microsoft 365.
Veeam Data Cloud for Microsoft 365 protect and restore hundreds of TBs of data or 10,000-plus objects. It “offers bulk restores at scale, ensuring increased resilience to ransomware or malware attacks and minimizing downtime.”
John Jester
John Jester, Chief Revenue Officer (CRO) at Veeam, said in a statement: “This new release combines the benefits of Veeam’s industry-leading technology – in both data protection and ransomware recovery – with the latest Microsoft 365 data resilience capabilities introduced by Microsoft, and extends them to even more customers using Microsoft 365. In addition, we’re making great progress in our joint innovation bringing the power and insights of Microsoft Copilot to the Veeam product family.”
Veeam has a new five-year strategic partnership with Microsoft and is developing future services integrating Microsoft Copilot and Azure AI services to enhance data protection for Microsoft 365 and Azure. These “will simplify operations [and] automate administrative tasks.”
Veeam Data Cloud for Microsoft 365 with Microsoft 365 Backup Storage will be available in early August. Three packaging options (Express, Flex, and Premium) are available to accommodate Veeam and Microsoft customers. Get more information here.
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Microsoft 365 Backup is offered through the Microsoft 365 admin center and is sold as a standalone pay-as-you-go (PAYGO) solution with no additional license requirements. There is more information available in a Microsoft blog.
The Cohesity, Rubrik, and Veritas offerings with integrated Microsoft 365 Backup Storage will be coming soon.
Data protector Acronis has shared research from the first half of 2024 in its biannual cyberthreats report, “Acronis Cyberthreats Report H1 2024” The report found that email attacks have seen a 293 percent increase when compared to the same period in 2023. The number of ransomware detections was also on the rise, increasing 32 percent from Q4 2023 to Q1 2024. In Q1 2024, Acronis observed 10 new ransomware groups who together claimed 84 cyberattacks globally. Among the top 10 most active ransomware families detected during this time, three highly active groups stand out as the primary contributors, collectively responsible for 35 percent of the attacks: LockBit, Black Basta, and PLAY.
Bahrain, Egypt, and South Korea were the top countries targeted by malware attacks in Q1 2024
28 million URLs were blocked at the endpoint in Q1 2024
27.6 percent of all received emails were spam and 1.5 percent contained malware or phishing links
The average lifespan of a malware sample in the wild is 2.3 days
1,048 cases of ransomware were publicly reported in Q1 2024, a 23 percent increase over Q1 2023
Top barriers to adopting AI were worries about the security and compliance risks that AI presents (74 percent), not having the proper training or talent to manage AI tools (38 percent), and AI tools being too expensive (26 percent).
While 94 percent of respondents said that they trust their data, 55 percent also said they would rather get a root canal than try to access all of their company’s data.
The top use cases for AI included improving customer experiences (60 percent), increasing operational efficiency (57 percent), and expediting analytics (51 percent).
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Data lake service provider Cribl has launched its inaugural Navigating the Data Current Report, which provides insights into how IT and Security teams are modernizing their data management practices. Cribl says it holds the largest amount of data on how IT and Security teams use their telemetry data and how the trends are shifting. A sampling of the key findings:
Increase in Data Sources: The number of data sources ingested by IT and Security teams grew by 32 percent year over year, with nearly 20 percent of users consuming from ten or more data sources.
Preference for Single Cloud: Contrary to the multi-cloud trend, only 11 percent of IT and Security teams are sending data to more than one CSP-native destination.
Growth in Multi-SIEM Deployments: The number of companies sending data to multiple SIEM products increased by 45 percent over the last year. Usage of multiple SIEMs grew from 11 percent to 16 percent, with significant gains for Google SecOps and Microsoft Sentinel.
You can download the full report at the link here and the accompanying blog post here.
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SSD supplier DapuStor says it is expanding its collaboration with Marvell to deliver breakthrough Flexible Data Placement (FDP) technology optimized for Quad-Level Cell (QLC) and Triple-Level Cell (TLC) SSDs. QLC SSDs face challenges such as lower endurance and slower write speeds. TheFDP technology supported in the Marvell Bravera SC5 SSD controller with firmware developed by DapuStor directly addresses these issues. The FDP algorithms dynamically adjust data placement based on workload and usage patterns, ensuring that the most frequently accessed data is stored in the fastest and most durable regions of the SSD. Testing has demonstrated that this can achieve a write amplification (WA) close to 1.0. Marvell and DapuStor will showcase the DapuStor QLC SSD H5000 series with FDP solutions at the 2024 FMS: Future of Memory and Storage conference in Santa Clara this month.
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According to Datacenter Dynamics Google has launched an air-gapped version of Google Distributed Cloud (GDC) hardware for artificial intelligence (AI) edge computing.It’s an appliance that runs Google’s cloud infrastructure stack including Kubernetes clusters, data security services, and Vertex AI platform when access to the Internet is not possible. It was in preview mode last year and is now GA. This Azure Stack-like offering has hardware components supplied by Cisco, HPE, Dell, and Nvidia (GPUs).
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ExaGrid has begun a new strategic partnership with StorIT Distribution, a Value-Added Distributor in the Middle East and North Africa for Enterprise IT Products and Systems. StorIT in partnership with ExaGrid will help organizations to have a comprehensively secure data backup that enables fast ransomware recovery, through its Tiered Backup Storage offering.
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Data pipeline supplier Fivetran announced the opening of its new EMEA headquarters in Dublin. Over the past three years, Fivetran has increased headcount in EMEA by almost 200 employees. Recruitment continues across Dublin, London, Amsterdam, Munich and its latest European location in the Serbian city of Novi Sad.
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Harriet Coverston
FMS: The Future of Memory and Storage, the world’s conference highlighting advancements, trends, and industry figures in the memory, storage, and SSD markets, announced that Harriet Coverston, CTO and co-founder of Versity Software, has won the SuperWomen of FMS Leadership Award for 2024. Her work included developing the Quick File System (QFS), which laid the foundation for SAM-QFS. SAM-QFS was the first multi-node clustered file system combined with archiving, addressing the high-performance scaling needs of large real-time streaming data systems.
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NVMe storage system provider HighPoint Technologies announced a strategic partnership with Phison. It says this collaboration promises to revolutionize compact PCIe storage systems for x86 server and workstation platforms. HighPoint’s NVMe Gen5 x16 Switch & RAID AIC Series, when equipped with Phison-powered M.2 NVMe SSDs, delivers the world’s fastest, densest, secure and field-proven PCIe Gen5 x16 NVMe storage in today’s marketplace, suitable for a wide range of applications and workflows including Data Centers, Professional Workstations, SMB platforms and personal computing.
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HighPoint Technologies says it will unveil the Industry’s first PCIe Gen5 x16 NVMe SSD systems to deliver nearly 60GBps of real-world transfer performance and up to 2 Petabytes of storage capacity from a single PCIe slot at FMS 2024. It will be introducing its Real-time Advanced Environmental Sensor Logging & Analysis Suite,“which enables customers to take a proactive approach to NVMe storage management by closely monitoring the hardware environment in real-time.”
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Cybersecurity company Index Engines announced new data integrity reporting within the CyberSense product to empower data protection and security teams to quickly understand and explain the health status of protected data. CyberSense indexes over 5 exabytes of customer data daily. The new data integrity homepage within the CyberSense 8.7 user interface displays insights into the data that CyberSense has analyzed, details on its integrity, and assurance that triggered alerts are addressed. This content is easily integrated into security platforms to collaborate on data protection and cybersecurity strategies.
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Enterprise cloud data management company Informatica announced its Q2 FY 24 earnings, beating guidance across the board, and demonstrating “that Informatica continues to be the go-to cloud data management platform to help enterprises prepare their data for GenAI.” Highlights from the quarter include:
Total Revenue: Increased 6.6 percent to $400.6 million
Total ARR: Increased 7.8 percent to $1.67 billion
Subscription ARR: Increased 15 percent year-over-year to $1.20 billion
Cloud Subscription ARR:Increased 37 percent year-over-year to $703 million
These results follow the general availability of CLAIRE GPT – the first GenAI-powered data management assistant now available in North America – as well as expanded partnerships with Microsoft, Snowflake and Databricks.
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Kioxia Kitakami plant with K2 on the left and K1 on the right
Kioxia announced that the building construction of Fab2 (K2) of its Kitakami Plant was completed in July. K2 is the second flash memory manufacturing facility at the Kitakami Plant in the Iwate Prefecture of Japan. As demand is recovering, the company will gradually make capital investments while closely monitoring flash memory market trends. Kioxia plans to start operation at K2 in the fall of 2025. Wedbush analyst Matt Bryson tells subscribers “Per the Nikkei, Kioxia’s plan of record now calls for production to commence in the second Iwate fab in 2025. We believe the ramp of the new fab likely will coincide with the more significant ramp of Kioxia and WD’s BICS8 products. And assuming the Nikkei is correct with its assertion around timing of the fab, it suggests to us that incremental NAND bits from the JV partners will remain at minimal levels until CQ4 of next year. With other vendors, in our view, adopting a similar stance regarding incremental production, we believe NAND fundamentals are set to remain favorable for producers for a prolonged period.”
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Canadian cloud and IaaS provider Leaseweb announced an object storage service with “highly competitive pricing”, S3 compatibility, Canadian data sovereignty and 99 percent uptime by redundantly spanning data across three availability zones. More info here.
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Dr Tolga Kurtoglu
Lenovo has appointed Dr Tolga Kurtoglu as the company’s new CTO, succeeding Dr. Yong Rui. Dr. Kurtoglu brings experience from his previous roles at HP, Xerox Palo Alto Research Center, and Dell. His expertise lies in AI, automation, and digital manufacturing. Dr. Kurtoglu will lead Lenovo’s Research team and innovation ecosystem, ensuring alignment with the Group’s technological vision and business objectives. He will also join Lenovo’s Executive Committee. Lenovo has established a new Emerging Technology Group, led by Dr. Yong Rui, which will focus on identifying and leveraging emerging tech trends to fuel future business growth.
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Lenovo has joined forces with Databricks to drive AI adoption amongst its customers. As a Databricks Consulting & Systems Integration (C&SI) Select Tier Partner, Lenovo will work with businesses to help them take advantage of enhanced data management capabilities, streamlining access to data from multiple sources and removing barriers to successful AI usage.
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MemVerge tells us that, during the last year, the CXL vendor community has piqued the interest of IT organizations with the promise of products offering more memory bandwidth and capacity for their memory-intensive apps. During the last year we’ve seen announcements for CXL memory modules and software support from MemVerge, Red Hat, and VMware. The last breakthrough needed get CXL technology into customer’s hands is the availability of servers. At FMS, August 6-8 in Santa Clara, you’ll see in booth MemVerge #1251 a server from MSI that’s integrated with memory peripherals and software, and available for Enterprise PoCs. In fact, the server on the show floor is being shipped to a multi-billion-dollar corporation immediately after the event.
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Cloud file services supplier Nasuni announced a couple of award wins in recognition of their customer service. For the fourth year running, it’s received a NorthFace ScoreBoard Service Award. Nasuni achieved an “excellent” NPS score of 87. For its overall technical support, Nasuni achieved an SBI rating of 4.8 and a 9.5/10 CSAT rating. Nasuni has also achieved eight Badge Awards in G2’s 2024 Summer Reports in categories including Hybrid Cloud Storage Solutions and Disaster Recovery.
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SSD and SSD controller supplier Phison will show an affordable, reliable, secure in-house AI training solution, aiDAPTIVE+, powered by its Pascari SSD, at FMS 2024 this month. It features:
An inhouse LLM system that can be trained and maintained on premises
A cost of $40K plus fees for electricity and power
Organizations fully own their data and can fine tune it
A turnkey solution: No additional IT or engineering staff is required to run it
Minimized security risks
Over 100 enterprises are using it with 12 distinct use cases in less than a year
Removal of universal pain points like onboarding new employees, ongoing professional development, keeping up with coding demands, and automation of tasks that can keep up with huge data volumes.
Trained data that reveals valuable inferences to deliver immediate business value
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Wedbush analyst Matt Bryson gave subscribers his view of Samsung’s latest and solid memory results
View: Samsung’s memory results and outlook appeared in-line to slightly better than expected.
DRAM bits lifted mid single digits (in line with expectations) while ASPs lifted in the high teens.
NAND bits lifted mid single digits (slightly above the prior guide) with ASPs up in the low 20 percent range.
Samsung guided for low single digit bit growth in both categories in CQ3.
HBM revenue lifted 50 percent Q/Q with Samsung indicating sales will continue to lift through the 2H (with 2H sales expected to be 3.5X 1H results). Management also is looking at HBM capacity doubling into 2025, with some suggestion they might have to lift their capacity plan further to meet customer demand.
HBM3E sales roughly tripled Q/Q. Samsung shipped samples of HBM3E (8H) and expects mass production to start this quarter. HBM3E (12H) has sampled with Samsung noting supply will expand through the 2H. HBM3E sales are expected to represent a mid-teens percentage of HBM revenue in CQ3 and around 60 percent of HBM3E sales in CQ4.
Samsung noted industry capacity adds for standard products appear to remain limited given modest capex and the focus on HBM.
We believe Samsung’s results (in particular slightly better NAND volumes and strong pricing) will be seen as positive for US memory vendors, particularly post Hynix’s results and subsequent concerns NAND demand in particular might be slowing.
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Leostream and Scale Computing today announced a joint integration delivering virtual desktop infrastructure (VDI) for distributed enterprise, SME/SMB, education, and others for whom VDI has been unrealistically complicated. As an alternative to VMware Horizon they have developed a streamlined, complete hardware/software architecture for hosting Windows and/or Linux desktops with greater ease of deployment, best-in-class support, and a competitive price point. Features include:
Robust tools for dynamic resource allocation to allow more efficient use of hardware and better handling of peak loads compared to more static allocation methods
Support for hybrid cloud environments with seamless management of both on-premises and cloud-based resources
Optimized performance for different workloads ensures that applications run smoothly, providing a better end-user experience
High availability features for minimal downtime and reliable access to mission-critical applications
Advanced security features such as multi-factor authentication, role-based access control, and end-to-end encryption, ensuring that virtual environments remain secure
Granular control over virtual environments that allows administrators to fine-tune settings according to specific needs
Support for custom configurations and automation to streamline operations and reduce administrative overhead
Support for the widest range of remote display protocols to meet the needs of even specialty applications
Highly intuitive and centralized management console that is easier to use than more fragmented approaches
Real-time analytics and monitoring tools for deep insights into the performance and usage of virtual resources
Streamlined installation and configuration for intuitive, fast setup – less than four hours from start to finish
Dedicated and responsive support
A more cost-effective licensing model to optimize IT budgets
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SK hynix introduced what it claimed is the industry’s best GDDR7 memory with improvement of 60 percent in operating speed (32 Gbps) and 50 percent in power efficiency. The company increased the layer number of the heat-dissipating substrates from four to six, while applying the EMC (Epoxy Moulding Compound) for the packaging material in a bid to reduce thermal resistance by 74 percent, compared with the previous generation, while maintaining the size of the product unchanged. Speed can grow up to 40Gbps depending on the circumstances. When adopted for the high-end graphics cards, the product can also process data of more than 1.5TB per second. It will be mass produced in 3Q24.
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Storage supplier Swissbit introduces its first PCIe Gen5 SSD; the D2200 series offering performance of up to 14 GBps for sequential read and 10 GBps for sequential write. It has a sequential read performance of up to 970 MB/s per watt.The series supports NVMe 2.0 and OCP 2.0, making it future-proof. Comprehensive data protection features, including TCG Opal 2.0, are standard. The Swissbit D2200 will be available in U.2 and E1.S form factors with storage capacities of 8 TB and 16 TB in late August, with a 32 TB version in U.2 format following at the end of 2024.
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Enterprise application data management supplier Syniti announced Q2 2024 results:
Cloud ARR increased 26 percent year over year and was up five percent from Q1 2024.
Software bookings grew 28 percent over Q1 2024 and were up 23 percent from Q2 2023.
Total quarterly revenue was up 8 percent when compared to Q2 2023, with services revenue rising 11% from a year ago.
The company reported an EBITDA margin in the mid-teens.
More than 11 clients with over $1 million in bookings.
Strong software renewals at longstanding clients in aerospace and defence and manufacturing, life sciences, oil & gas.
Robust volume with its SAP business with more than 50 percent of software annual contract value coming from the partnership; Syniti’s software is sold as a SAP Solution Extension and is available as an Endorsed App on SAP Store.
The company expanded its client base with 15 significant new logos in the quarter, adding five more Global2000 organizations to its customer roster including major players in retail, food & beverage, manufacturing and life science.
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Andre Carpenter
Cloud storage provider Wasabi has appointed Andre Carpenter as the managing director of its Australia and New Zealand business as the company continues to rapidly expand in the region. Cloud storage adoption is growing in Australia, with 89 percent of Australian organizations expecting to increase the amount of data they store in the public cloud in 2024, according to the APAC Wasabi Cloud Storage Index. Most recently, Andre was the APAC director of cloud solutions at Crayon, a global technology and digital transformation service company. In addition, Andre has also held leadership roles across sales and consulting for global technology companies including Dell EMC, Hewlett Packard Enterprise, NetApp, Oracle and Veeam.
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AI-native vector database company Weaviate is releasing a developer “workbench” of tools and apps along with flexible tiered storage for organizations putting AI into production. They include:
Recommender app: Provides a fully managed, low-code solution for rapid development of scalable, personalized recommendation systems. Recommender offers configurable endpoints for item-to-item, item-to-user, and user-to-user recommendation scenarios and supports images, text, audio and other forms of multimodal data. Sign up to be part of the private beta.
Query tool: Enables developers to query data in Weaviate Cloud using a GraphQL interface. Available now through Weaviate Cloud Console.
Collections tool: Allows users to create and manage collections in Weaviate Cloud without writing any code. Available now through the Weaviate Cloud Console.
Explorer tool: Lets users search and validate object data through a graphical user interface (GUI). Coming soon to Weaviate Cloud Console.
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ReRAM developer Weebit Nano has taped out its first chip in DB HiTek’s 130nm BCD process. It’s targeting completion of qual/production readiness in the 2nd calendar quarter of 2025. DB HiTek customers can get started now. On Aug. 7, during FMS: The Future of Memory and Storage, Weebit’s VP of Quality & Reliability Amir Regev will share the latest Weebit ReRAM technical data including results on GlobalFoundries 22FDX wafers – the first such ReRAM results.
Big Blue developed its own Vela cluster, using Storage Scale, to train its AI models.
IBM’s next generation AI studio, watsonx.ai, which became generally available in July of 2023, was trained on the Vela cluster. Storage Scales is a parallel filesystem and Vela uses it as a quasi-cache in front of object storage, speeding data IO to keep GPUs busy.
The Vela infrastructure powering IBM’s Gen AI model development is described in a freely available research paper.
It describes Vela as a cluster of CPU/GPU servers hosting virtual machines in the IBM Cloud. The server nodes are twin-socket systems with, originally, Cascade Lake Gen 2 Xeon Scalable processors plus 1.5TB of DRAM and 4 x 3.2TB NVMe SSDs, 8 x 80GB Nvidia A100 GPUs, using NVLink and NVSwitch. The Xeons were later upgraded to IceLake.
A 2-level spine-leaf Clos structure, (nonblocking, multistage switching network) based on 100Gbps network interfaces, links the nodes together. The storage drives are accessed over Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) and GPU-direct RDMA (GDR). GDR with RoCE allows GPUs on one system to access the memory of GPUs in another system, using Ethernet network cards. Congestion management is built into the networking subsystem.
Vela is operated by IBM Cloud as IaaS (Infrastructure as a Service). Red Hat OpenShift clusters are used for tasks that span the entire AI lifecycle, from data preparation to model training, adaptation, and ultimately model serving.
The data needed for the AI training is held in object storage but this is too slow for both reading (needed for job loading) and writing (needed for job checkpointing). The IBMers decided to use Storage Scale: “a high-performance file system … inserted between the object storage and the GPUs to act as an intermediating caching mechanism. In doing so, the data can be loaded into the GPUs much faster to start (or re-start) a training job, and model weights can be checkpointed to the file system at a much faster rate than when checkpointing directly to object storage. Thanks to unique technology in the file system we use, the checkpointed data can then be asynchronously sent to object storage but in a way that does not gate progress of the training job.”
A Scale client cluster runs across Vela’s GPU nodes in container-native mode leveraging the CNSA edition of Scale. The paper states Vela: “uses Kubernetes operators to deploy and manage Scale in a cloud-native fashion as well as a CSI Plugin for provisioning and attaching persistent volumes based on Scale. The client cluster does not contain any locally attached storage devices and instead performs remote mount of the file system in the storage cluster. Such an architecture allows compute and storage clusters to grow and shrink independently as workload requirements change.”
It says: “We configure Active File Management(AFM) technology to transparently connect filesets to object storage buckets. File system namespaces represent objects in buckets as files and brings data from the object storage into the file system on demand. When a file is written to the file system, AFM eventually moves it to object storage.”
The total capacity of this Scale parallel file system, using all attached devices, is hundreds of TBs.
The research paper says: “Scale is deployed in Vela using a disaggregated storage model. The dedicated Scale storage cluster consists of tens of IBM Cloud Virtual Server Instances (VSIs) with two 1TB virtual block volumes attached to each instance. The virtual block volumes are hosted on a next-generation cloud-native and highly performant block storage service in IBM Cloud that can meet the high throughput requirements of model training workloads.”
We’re told by a source close to IBM that before it deployed this storage solution based on Scale, “AI researchers using Vela could either use IBM COS directly or an NFS file system that was deployed for Vela.
“Compared to NFS performance, our Scale file system achieves a near 40x read bandwidth speedup (1 GBps vs 40 GBps with Scale), which directly helps with input data read operations. Also compared to IBM COS bandwidth, the Scale file system achieves a near 3x write bandwidth speedup (5 GBps vs 15 GBps with Scale), which accelerates the checkpoint and other data write operations.”
This was based on data from iteration times for a Granite-13B AI training job using NFS and another Granite-8B job using the Scale file system.
Training jobs can take a month or more to run, as a table in the paper indicates:
Vela was overhauled in 2023 with the larger and more powerful Blue Vela cluster, which came online in April this year and was built with Dell and Nvidia. We’ll describe this in a second article.
Panmnesia is developing technology enabling the connection of hundreds of memory devices across multiple servers.
The Korean startup supports v3.1 of Computer Express Link (CXL). This addresses remote memory pooling and PCIe 6.0 interconnects to enable servers, both CPU and GPU-based, to share external memory, combining it with their own direct-attached DRAM to increase memory capacity and their ability to run memory-bound AI workloads, Panmnesia says.
Myoungsoo Jung
Panmnesia CEO Myoungsoo Jung said in a presentation: “Our CXL 3.1 switch will not only reduce server operating costs but also enable practical memory expansion in terms of performance, thanks to our high performance CXL IP.”
CXL 3.0 enables a pool of CXL switch-accessed external memory to be shared between host computers with coherent caches in the hosts and the CXL memory endpoints or expanders. CXL 3.1 added several incremental features to this base:
CXL fabric improvements and extensions with, for example, scaleout CXL fabrics using Port-Based Routing (PBR)
Memory expander improvements with up to 32-bit of metadata and RAS capability enhancements
Earlier this year, Panmnesia introduced a CXL-enabled AI accelerator. It is developing a v3.1 CXL switch chip, codenamed Shattuck, and System on Chip, and claims its CXL 3.1 IP has latency below 100 nanoseconds, which would be a world first.
The switch chip connects various system components such as CPUs, GPUs, memory, and accelerators, managing end-to-end communication between them. With a cascading, multi-level concept, it will allow multiple switches to be connected in numerous levels or configured in a fabric-like structure, enabling the connection of hundreds of devices across multiple servers. And it will support previous CXL standards, type 1, 2, and 3 devices, and all subprotocols of CXL – CXL.mem, CXL.cache, and CXL.io.
Get more details of Panmnesia’s CXL 3.1 technology here (registration required).
Panmnesia plans to provide its CXL 3.1 switch chip to customers by the second half of 2025.
Revenues in Western Digital’s fourth fiscal 2024 quarter, ended July 341, were $3.76 billion, 41 percent higher year-on-year and beating its $33 billion outlook, with a $330 million profit contrasting vividly with the year-ago loss of $715 million. Full fy2024 revenues rose 6 percent to $13 billion.
The 41 percent revenue growth was more than last quarter’s 29 percent increase as the firm’s exit from the flash/HDD sales trough quickened.
A statement from David Goeckeler, Western Digital CEO said: “Together, with the structural changes we have made to strengthen our operations, we are benefitting from the broad recovery we are seeing across our end markets and structurally improving through-cycle profitability for both Flash and HDD.”
WD has a way to go before it regains its fy2018 revenue high point
He believes: “The emergence of the AI Data Cycle marks a transformational period within our industry that will drive fundamental shifts across our end markets, increasing the need for storage and creating new demand drivers.”
Financial Summary
Gross margin: 35.9 percent vs year-ago 36.3 percent
Operating cash flow: $366 million
Free cash flow: $282 million
Cash & cash equivalents: $1.9 billion
EPS: $0.88
WD sells into the cloud (enterprise and CSP), client (PC and notebook) and consumer (reail) segments. Cloud, 50 percent of its revenues at $1.88 billion, was up 89 percent year-on-year due to”higher nearline shipments and pricing in HDD, coupled with increased bit shipments and pricing in enterprise SSDs.”
Note the gradual transition of client to cloud revenues
Client was 32 percent of its revenues at $1.2 billion; a 16 percent increase, attributed to an “increase in flash ASPs offsetting a decline in flash bit shipments while HDD revenue decreased slightly.”
The consumer segment brought in 18 percent of total revenues and was up just 5 percent Y/Y from “improved flash ASPs and bit shipments.” Sequentially it was down 7 percent because of “lower flash and HDD bit shipments partially offset by higher ASPs in both flash and HDD.”
Overall flash (SSD) revenues were $1.76 billion; a rise of 27.9 percent Y/Y, while disk (HDD) revenues rose 27.9 percent to $2 billion, up 54.7 percent, as cloud buyers bought many more nearline drives.
WD noted QLC-based client SSDs grew 50 percent on a sequential exabyte basis. It’s sampling a 64 TB eSSD with plans for volume shipment later this calendar year, and it’s qualifying a PCIe Gen 5 based eSSD at a hyperscaler with ramp expected in the second half of this calendar year. This SSD has “the best read performance and really good power efficiency.”
Goeckeler said in the earnings call that the race for layer-count increase-driven SSD capacity had ended: “The layers focus race is behind us. The emphasis is now shifting toward strategically timing the economic introduction of new longer-lasting nodes. Innovation now means enhancing power efficiency, performance, and capacity within these nodes, while capital decisions increasingly prioritize opportunities for margin expansion and revenue growth.”
It saw more disk-based revenues than Seagate for the second consecutive quarter, as Seagate’s delayed HAMR transition let WD keep its lead:
WD has shipped samples of a 32 TB UltraSMR ePMR nearline hard drive to select customers. HDD unit ships increased for the third successive quarter to 12.1 million with the disk ASP rising to $163.00 from last quarter’s $145.00 and the year-ago $99.00. Goeckeler said that, with the 32 TB drive, this is the “first time anybody’s crossed the 30 TB at scale” point, alluding to Seagate’s inability to ship its Mozaic 3+ drives at scale.
HDD revenue growth depends upon capacity increases, with Goeckleler observing: “The HDD business has undergone a remarkable transformation in recent quarters, marked by strategic initiatives aimed at introducing the most innovative, high-capacity products to market. …we are well-positioned to deliver the industry’s highest-capacity hard drives and the best TCO. …Our cloud customers continue to transition to SMR, and we anticipate a third major cloud vendor to begin the ramp of adopting SMR in the fiscal first quarter.”
He added: ”We’ve got good supply demand balance that’s giving us good visibility throughout the rest of the calendar year, we pretty much know where every drive is going to go at this point. We made a really significant transition this past quarter, in that we moved up our request to our customers to give us visibility 52 weeks ahead, so a 52-week lead time on HDDs. Now, the reason that’s so important is the cycle time on a net to build an HDD is about 50 weeks.”
“Our customers have responded well to that request, and we now have visibility for the entire fiscal year from a number of our biggest customers.” WD is working on the rest of its customers to do the same.
It noted a legal defeat with cost implications, saying “On July 26, 2024, a jury awarded a lump sum of $262 million against a subsidiary of the company in a patent infringement action. …In addition to the lump sum amount, the company anticipates that the plaintiff will request costs, attorney’s fees and interest.” It will appeal the judgement.
The revenue number in next quarter’s outlook is $4.1 billion +/- $0.1 billion, a 49 percent Y/Y increase at the mid-point.
Goeckeler emphasized WD’s AI-driven growth outlook: ”As we enter fiscal year 2025, we are well-positioned to capture the long-term growth opportunities in data storage and believe the AI Data Cycle will be a significant incremental growth driver for the storage industry.”
In the flash market: “We still see demand outstripping supply through the second half of the year. And quite frankly, our modeling shows throughout next calendar year as well.”
WD is progressing its coming split into separate HDD and NAND/SSD businesses. It anticipates “beginning to incur separation dis-synergy costs in the second half of the calendar year.” These will be operating expense costs, not expenses in the cost-of goods area.
HPC storage supplier VDURA is claiming that disk drive arrays are more environmentally friendly than all-flash arrays in terms of their total carbon emissions.
Eric Burgener
VDURA – the rebranded Panasas – supplies both SSD and HDD-based storage systems. The starting point for this environmental argument was a Pure Storage blog written by Eric Burgener, its technical strategy director, which argued: “When you conduct a system-level comparison, flash-based systems can have a notably lower CO2e than HDD-based systems.”
CO2e means carbon dioxide equivalent. Burgener looked at 12 and 22TB HDDs and compared their carbon load to commercial SSDs and Pure’s own Direct Flash Modules (DFMs), concluding: “If you use storage systems from Pure Storage and evaluate CO2e across an enterprise storage system’s entire life – which I would strongly argue is the right way to evaluate it – it’s true that all-flash systems can have a lower embodied carbon impact than HDD-based systems.”
However, VDURA senior systems architect and platform lead Michael Barrell had a look at Pure’s blog and came to a different conclusion. “My analysis shows that a modern HDD system could be 60 percent more favorable at launch in terms of CO2e emissions. When factoring in a more realistic failure rate the edge is maintained at 54 percent.”
Michael Barrell
Barrell’s analysis considers a 22TB HDD and then projects what a 30TB HDD system would look like, both based on using 78-slot 4RU drive cabinets to build a 4PB system. This is a denser enclosure than the one used in Pure’s analysis, and Barrell notes: “A 22TB-based system comes out to be 14U, which is 56 percent more dense than the original Pure perspective. If you push this even further with the latest HDD technology from Seagate and use a Mozaic3 30TB [HDD], you can see a 68 percent reduction in rack space.”
Barrell argued he believes Pure’s argument is based on a 12TB HDD emitting 14.4kg of CO2e a year. He claims “A 22TB HDD is 40 percent lower in CO2e emissions than a 12TB HDD on a per TB basis. When a 30TB HDD is considered, my estimate is that it could be up to 50 percent lower in terms of CO2 emissions than a 12TB HDD. Significant improvements are continually being made.”
On that basis, “we see that a 22TB system could be as much 67 percent lower in CO2e emissions than a Pure-based DFM system at initial deployment. A 30TB-based system could be almost 73 percent lower.”
To calculate lifetime drive emissions in a system, Pure’s Burgener estimated HDD replacement numbers in a 1EB system built using 22TB drives – 45,504 HDDs – using a Backblaze-sourced 1.41 percent annual failure rate (AFR) for HDDs. He calculated that this would require replacing 641 disk drives every year.
Table from Burgener’s blog
Barrell doesn’t agree. “For my modeling, I assume that after five years the AFR increases greatly and continues until the ten-year mark. My model uses five percent for the first year and increases one percent for each year after. When you use this model, a more reasonable estimate of total devices used can be calculated. My calculations show that you would only replace 78HDDs in the 22TB model. When you factor this into the overall CO2e impact, the HDD-based system maintains a significant edge with a 54 percent improvement in emissions and a 30TB HDD projected a margin of 62 percent.”
He concludes: “My analysis shows that by applying a more logical thought process we see that a modern HDD-based system, with dense drives and dense enclosures, starts at 54 percent better in terms of CO2e emissions and moves to over 60 percent as the newest 30TB HDDs come to market.”
As a kicker, he notes he made his analysis “without even considering the price delta between these sorts of systems.”
“Did we want to discuss the storage and memory needs for AI PCs?” asked Kingston. Yes, B&F did, and so we interviewed Elliott Jones, who looks after B2B Strategic Marketing at Kingston.
Kingston Technology, headquartered in Fountain Valley, CA, is a global manufacturer of memory devices, SSDs and USB-connected storage devices. It’s the largest independent DRAM module producer, employs more than 3,000 people and has factories and logistics bases in manufacturing and logistics facilities in the United States, United Kingdom, Ireland, Taiwan, and China.
The AI PC is reckoned to need more memory and storage capacity than an average PC because AI workloads are more intensive and demanding than running a spreadsheet or word processor but, apart from a 40+ TOPs (Tera Operations per second) requirement there doesn’t appear to be a basic specification for the device.
Blocks & Files: What is the background to the AI PC development and what components will an AI PC have, in general, that are different from an average PC?
Elliott Jones.
Elliott Jones: Window AI PCs have been around in various forms since late last year, but they are still in a transitional phase. That said, AI PCs offer way more autonomy than their cloud counterparts. For one, they have NPUs connecting with memory vs. the HBM/DRAM memory that their server counterparts offer.
Blocks & Files: Is there a real need for an AI PC or is it just marketing hogwash aiming to boost a PC refresh surge for suppliers?
Elliott Jones: Both — it’s a case of hype and purpose. It’s no secret that OEMs have been trying to push new PC/laptop purchases after a significant drought.
However, that doesn’t mean AI PCs are devoid of value. By design, they offer autonomy and privacy over data and the promise of employee efficiency on a local basis. But beyond current creative, video conferencing, and early Co-Pilot applications, there is much to be found out. This will come, and we will see the benefits — but there are hurdles ahead.
So today, it’s somewhat limited — the hardware appears to be ahead of the software, so there is much to do. That’s why we are advocating short-term upgrades — to buy organizations time to figure out how the tech and market moves.
Blocks & Files: Is there a reference architecture for an AI PC?
Elliott Jones: Not really — we see AI PCs progressing without reference architecture. This is evident in the market today. AI PC laptops offer just 256GB storage with 8GB memory, which is woefully under specced. While we have seen Co-pilot+ come out with minimum specifications — even then they admit these specs will change with the inevitable demands of software applications. In short, if you are buying today, make sure it is over specced and you can upgrade; otherwise, your devices will get found out in a short amount of time.
Blocks & Files: Who should develop it? Will this define a standard AI application set it will have to support and run?
Elliott Jones: The problem is that ARM CPUs are now competing with x86 CPUs — it’s very much an arms race on TOPs (pun intended). While ARM CPUs appear faster and have more TOPs, this offers no guarantee for the future. AMD/Intel will likely be trading blows between themselves and ARM.
The other challenge for ARM is that it is trying to displace x86 chipsets. For organizations with internal applications, there is no guarantee they will work with ARM CPUs — even with the Prism emulator.
Taking a step back — things will likely converge — certainly with the x86 chipsets.
Blocks & Files: In the absence of an AI PC reference architecture, what can you say about likely AI PC memory requirements?
Elliott Jones: The biggest shift is whether users or organizations will swallow the inability of not being able to upgrade. With “right to repair” growing in popularity, in turn, it suggests “right to upgrade” will be next. At Kingston, we anticipate CAMM2 (Compression-Attached Memory Module) becoming a potential player in this discussion. Some OEMs will elect to have soldered memory. But they are exchanging short term wins –the benefits of selling to those who are unsure – with an upgradable device.
Blocks & Files:In the absence of an AI PC RA, what can you say about likely AI PC storage requirements?
Elliott Jones: In the past few days, I have heard reports that OEMs are using cheaper DRAM-less storage, and the existing native AI PC applications are suffering as a consequence. This indicates a misunderstanding or confusion in how AI PCs could or should work, not only today – but also in the future. If major challenges are being realized today – it offers a scathing view of what will happen in the medium and long term – as organizations battle with their refresh objectives.
Blocks & Files: Do you think an AI PC reference architecture will emerge?
Elliott Jones: A common ISV portfolio would have been the answer – but that view changed since the inclusion of the ARM chipset with Windows 11. We have now a potential Android vs iOS moment – bigger ISVs can cope with developing for both platforms, while the smaller ISVs will have to pick a route. Factor in that Windows 10 will become EOS in October next year, and organizations will find themselves in a pinch. For AI PC RA to be realized – it requires common ground – but this might not be likely in the short term due to x86 and ARM platforms competing for market share.
Marvell has launched Structera brand CXL 2.0 memory expansion and acceleration products with a DDR4 DRAM recycling capability to enable its reuse as DDR5 memory gets installed.
Raghib Hussain
CXL (Computer eXpress Link) is the extension of the PCIe bus outside a server’s chassis, based on the PCIe 5.0 standard. CXL 2.0 provides for memory pooling between a CXL memory device and more than a single accessing host. It enables cache coherency between a server CPU host and three device types. Marvell reckons that X86-based data centers will find their servers running out of memory when executing Gen AI-type workloads such as deep learning recommendation models (DLRM) and other machine learning applications. Its Structera products ”enable terabytes of memory to be added to general-purpose servers and address high-capacity memory applications such as in-memory databases.”
Raghib Hussain, Marvell’s president of products and technologies, put out a statement: “Our new Structera CXL product line will be a game-changer in enabling optimal resource utilization and lowering energy consumption for scaling memory-intensive workloads in the cloud,” meaning, we understand, both private and public clouds.
There are two product lines, each supporting inline LZ4 compression/decompression and AES-XS 256-bit encryption. They also have embedded hardware security modules and a secure boot facility. The compression HW adheres to the Google and Meta specifications that have been submitted to the Open Compute Project (OCP).
The Structera A 2504 memory accelerator, which “integrate server-class processor cores and multiple memory channels with CXL to address high-bandwidth memory applications” for one or more servers. The Structera X 2404 and X 2504 products are “designed for expanding memory capacity per server to address high-capacity memory applications.“ Both are PCIe 5.0 interconnect designs.
The A 2504 comes with a 16-core Arm Neoverse CPU, up to 4TB of DDR5 memory capacity – using 32 x 128GB DIMMs – and supports up to 200 GBps memory bandwidth.
Marvell Structera A 2504 diagram
Marvell says that adding one Structera A 2504 to a single 64-core DLRM server would increase the number of compute cores by 25 percent (64 vs 80), aggregate memory bandwidth by 50 percent (400 GBps vs 600 GBps), total memory by up to 4TB, and memory bandwidth per core by 25 percent (6.25 GBps vs. 7.5 GBps. It would also improve memory bandwidth power efficiency from 1W to 0.83W per GBpsec.
Adding two A 2504s would increase compute cores by 50 percent, double aggregate memory bandwidth, increase memory capacity by up to 8TB, increase memory bandwidth per core by 50 percent, and improve memory bandwidth power efficiency to 0.75W per GB/sec.
The X 2404 and X 2504 memory pool products ditch the Neoverse Arm CPU but are otherwise much the same,. The X2404 supports 4 DDR4 memory modules, with 3 DIMMs per channel, while the X2504 supports a quartet of faster DDR5 chips and with 2 DIMMs per channel.
Marvell Structera X 2404 diagram. The X 2504 block diagram has DDR5 memory in its bottom layer and a 16 lane PCIe/CXL module in its top right box
Marvell claims that these “are also the first CXL memory-expansion products that can simultaneously support two server CPUs to optimize power, space and memory utilization.”
It makes the point that “the Structera X 2404 enables operators to recycle their DDR4 DIMMs from decommissioned general-purpose servers. Millions of functional DDR4 DIMMs are expected to become e-waste over the next few years as operators replace existing general-purpose servers with new ones utilizing DDR5.”
This means that “using “free” DDR4 DIMMs to expand the capacity in these servers reduces CAPEX by thousands of dollars per general-purpose server4. Repurposing decommissioned DDR4 DIMMs also addresses data center sustainability goals.”
Marvell will develops custom CXL silicon for public cloud operators that is optimized for their unique architectures and workloads.
The Structera release had supportive statements from AMD, Arm, Intel and Micron.
Structera A near-memory accelerators, Structera X memory-expansion controllers and custom CXL silicon will sample in the fourth quarter of 2024. Get a Structera A product brief here, a Structera X 2404 product brief here and an X2504 product brief here.
Commvault reached a quarterly sales record, with revenue growth accelerating, with the company claiming its focus on cyber-resilience paid off.
Revenues for the data protector in the quarter ended June 30 were up 13 percent year-on-year to $224.7 million, beating guidance by $10 million, with an $18.5 million profit, up 46.8 percent. Revenues also increased sequentially, by just 0.6 percent. Although the increase is tiny, seasonally Q1 revenues have not surpassed prior Q4 revenues since at least 2011.
Sanjay Mirchandani, president and CEO, said in prepared remarks: “Q1 was an outstanding quarter and start to our fiscal year. We saw great momentum across all our primary KPIs (Key Performance Indicators) this quarter.”
Sanjay Mirchandani.
Total ARR rose 17 percent to $803 million;
Subscription ARR accelerated 27 percent to $636 million;
SaaS ARR jumped 66 percent to $188 million;
He pointed out: “And we did this profitably while investing in growth initiatives and hitting record Q1 free cash flow margins.”
The cyber-resilience focus resonated with customers. “Commvault Cloud platform continues to accelerate our growth as more companies turn to us for industry-leading cyber-resilience.” He emphasized attack recovery testing: “Our Cleanroom Recovery offering enables businesses to easily, frequently, and affordably test their cyber recovery plans in advance in good times, across workloads, at scale, on demand! Nobody else does this.”
Financial Summary
Gross Margin: 82.3 percent
Operating cash flow: $44.7 million
Free cash flow: $43.8 million
Cash and cash equivalents: $287.9 million
Stock repurchases in quarter: $51,400,000
Commvault’s large customer subscription count reached 9,900, up 26.9 percent annually and 6.5 percent from the prior quarter, with steady growth in that count. Subscription customers now represent 65 percent of Commvault’s customer base.
William Blair analyst Jason Ader told subscribers: “Revenue from term software deals over $100,000 grew 13 percent year-over-year, driven by a steady stream of larger deals.”
He said: “The solid first-quarter performance was attributed to an accelerated volume of larger deals, strong subscription momentum, and continued go-to-market improvements (specifically around SaaS and cyber-resilience messaging).”
The outlook for FY2025’s second quarter is for revenues of $220 million +/-$2 million, an 8.4 percent rise Y/Y at the mid-point. The full year outlook is for revenues between $915 million and $925 million, a 9.3 percent increase at the mid-point. Commvault’s aspiration for FY2026 is $1 billion in ARR and $310 million to $340 million in SaaS ARR, a ~40 percent CAGR.
Ader said: “The company … raised full-year ARR growth guidance by one point, to 15 percent, with full-year subscription ARR growth expected to be 24 percent at the midpoint. Management’s confidence in accelerating growth is based on a more material contribution from cyber-resilience offerings (Air Gap Protect, Threatwise, etc.), solid renewals, improving sales execution, increasing pace of legacy vendor displacements (Veritas called out), and growing cross-sell opportunities (management specifically pointed to its autonomous recovery and cyber recovery bundles). Management also remains confident in its fiscal 2026 goal of $1 billion in ARR (with subscription ARR at 90 percent of this total).”
He pointed out a future revenue growth driver: “Commvault also received FedRAMP High authorization (enabling the company to sell into the defense and intelligence sector), pointing to the still untapped federal government opportunity for the company (CVLT Cloud now available on AWS Marketplace for US federal government).”
Edge HCI system vendor Scale Computing has seen sales take off as the reality of Broadcom’s VMware business changes affect VMware customer and channel loyalty.
It said increased demand for both edge computing and alternative virtualization platforms drove record growth for the company. Scale saw both new customers and partners double over the same period last year.
Jeff Ready, CEO and co-founder of Scale Computing, stated: “We are seeing our business explode to the upside in the aftermath of the Broadcom-VMware acquisition. In Q2, we closed an unprecedented number of new business opportunities, keeping us on pace to exceed 50 percent year-to-year revenue growth in 2024.”
Jeff Ready
Broadcom has changed VMware from perpetual to subscription licensing, potentially doubling customer costs or worse. VMware products were consolidated into two main bundles, VMware Cloud Foundation and VMware vSphere, increasing costs for some standalone product users.
Broadcom said this change represented “radical simplification across our portfolio, go-to-market, and organizational structure to make it easier to do business with us … Offering a few offerings that are lower in price on the high end and are packed with more value for the same or less cost on the lower end makes business sense for customers, partners and VMware. We’re putting all our R&D investment towards fewer offerings, which is a double win for customers.”
However, the changes are leading to churn in the VMware customer and partner populations. As a consequence Scale is positioning itself as a VMware alternative for both channel partners and customers – and it appears to be paying off.
Ready claimed: “This quarter we signed hundreds of new partners across all regions – North America, Latin America, EMEA, and APAC. We are committed to offering our partners and end-customers a true partnership, built upon the most innovative and resilient IT solutions on the market.”
New and current partners looking to switch customers to Scale Computing can participate in a “VMware Rip & Replace” promotion, receiving a 25 percent discount on Scale Computing software and services for each new customer implementation. VMware customers seeking alternatives can transfer existing software licenses and exchange hardware through the “Seamless Switch: Trade-Up to Scale Computing” promotion.
COMMISSIONED: Whether you’re using one of the leading large language models (LLM), emerging open-source models or a combination of both, the output of your generative AI service hinges on the data and the foundation that supports it.
The right mix of models combined with the right data, architecture and solutions can provide GenAI services that retrieve critical contextual information at lightning speed while reducing inaccuracies.
Balancing the right mix of technologies and techniques is a delicate dance. Technology implementation challenges, security concerns and questions about organizational readiness rank among the top barriers to success, according to recent Bain & Company research.
What architecture and toolset will help me build and test a digital assistant? How do I safely integrate my enterprise information into models? What platform will provide the best performance? And how do I avoid overprovisioning, a common sin from past deployment efforts?
These are frequently asked questions organizations must consider. There are no cookie-cutter approaches for your organization. Yet early trailblazers have found fortune with certain technologies and approaches.
RAG – fueled by a vector database
From digital assistants to content creation and product design, many corporations choose to tap their own corporate data for GenAI use cases. Among the most popular techniques for this is retrieval-augmented generation (RAG).
An alternative to fine-tuning or pre-training models, RAG finds data and documents relevant to a question or task and provides them as context for the model to provide more accurate responses to prompts.
A crucial ingredient of RAG applications is the vector database, which stores and retrieves relevant, unstructured information efficiently. The vector database contains embeddings, or vector representations, of documents or chunks of text. RAG queries this content to find contextually relevant documents or records based on their similarity to keywords in a query.
The pairing of vector database and RAG is a popular combination. Research from Databricks found that use of vector databases soared 376 percent year over year, suggesting that a growing number of companies are using RAG to incorporate their enterprise data into their models.
Synthetic data protects your IP
You may have reservations about trying techniques and solutions with which you and your team may not be familiar. How can you test these solutions without putting your corporate data at risk?
Synthetic data has emerged as a popular approach for organizations looking to support the development of autonomous vehicles, digital twins for manufacturing and even regulated industries such as health care, where it helps avoid compromising patient privacy.
Some GenAI models are partially trained on synthetic data. Microsoft’s Phi-3 model was partially trained on synthetic data and open source options are emerging regularly.
Regardless of whether you use synthetic data to test or real corporate IP, content created by GenAI poses risks, which is why organizations must keep a human in the loop to vet model outputs.
To the lakehouse
RAG, vector DBs and synthetic data have emerged as critical ingredients for building GenAI services at a time when 87 percent of IT and data analytics leaders agree that innovation in AI has made data management a top priority, according to Salesforce research.
But where is the best place to draw, prep and manage that data to run through these models and tools?
One choice that is gaining steam is a data lakehouse, which abstracts the complexity of managing storage systems and surfaces the right data where, when and how it’s needed. A data lakehouse forms the data management engine for systems that detect fraud to those that derive insights from distributed data sources.
What does a lakehouse have to do with RAG and vector databases? A data lakehouse may serve as the storage and processing layer for vector databases, while storing documents and data RAG uses to craft responses to prompts.
Many organizations will try many LLMs, SLMs and model classes, each with their trade-offs between cost and performance, and many will attempt different deployment approaches. No one organization has all the right answers – or the wherewithal to pursue them.
That’s where a trusted partner can help.
Dell offers the Dell Data Lakehouse, which affords engineers and data scientists self-service access to query their data and achieve outcomes they desire from a single platform.
Your data is your differentiator and the Dell Data Lakehouse respects that by baking in governance to help you maintain control of your data on-premises while satisfying data sovereignty requirements.
The Dell Data Lakehouse is part of the Dell AI Factory, a modular approach to running your data on premises and at the edge using AI-enabled infrastructure with support from an open ecosystem of partners. The Dell AI Factory also includes professional services and use cases to help organizations accelerate their AI journeys.
VAST Data is partnering with Vapor IO, a colocation provider of hyperlocal AI platform services in the US, to provide global and multi-format AI data storage, supporting structured and unstructured tabular data, including vectors.
Austin, TX-based Vapor IO has developed self-contained, high-density VEM micro modular datacenters as part of its edge colocation and interconnection facilities, which include both wireless and wired networking capabilities. Its Kinetic Edge combines multi-tenant colocation with software-defined interconnection and high-speed networking.
Vapor IO came out of stealth in 2015, the same year it closed a Series A funding round led by Goldman Sachs. With subsequent B and C-rounds, it has raised $90 million in total.
Zero Gap AI is a Vapor IO-operated 5G and AI-as-a-Service platform, providing private 5G and GPU-based micro-clouds adjacent to specific locations, such as retail stores, factory floors, and city intersections. These are so-called hyperlocal AI services, delivered over private wired and wireless networks without running servers on-premises yet with fast response times.
The local AI site is built from Supermicro servers presenting an Nvidia MGX device, based on the GH200 Grace Hopper superchip, and supporting 5G and AI running on the same machine. There are such sites in 36 US cities, including Atlanta, Chicago, Dallas, Las Vegas, and Seattle, in what Vapor IO calls its Kinetic Grid platform. There are private dedicated wired fiber links to Zero Gap AI customers. An existing last-mile network link could be used as well. A private 5G wireless network can be used inside a customer’s site to link to devices there.
Vapor IO VEM-20 pedestal datacenter
Cole Crawford, Vapor IO’s founder and CEO, stated: “Until now, the challenge has been the high costs and complexities of creating AI clusters in precise locations, not to mention managing the intricate dance of AI orchestration. For those aiming to achieve real-time inferencing, Zero Gap AI presents the ideal architecture. By eliminating the need for onsite AI and 5G hardware and leveraging private fiber from nearby access points, we’re simplifying the process and cutting costs, enabling a seamless adoption and expansion of AI capabilities for businesses and municipalities alike.”
Vapor IO suggests:
A multi-location retailer can use Zero Gap AI to deploy an AI-driven automated checkout system without putting expensive AI equipment in each store.
A municipality can use Zero Gap AI computer vision services to deploy a pedestrian safety capability to hundreds of busy intersections without putting equipment on every corner.
A manufacturer can use Zero Gap AI real-time inferencing to support highly reliable factory IoT, reducing risk and lowering capital expenditures.
The pitch here is that Zero Gap AI provides AI access points “across an entire market” with a distributed architure having mesh-like network grid for AI providing near-zero latency. It is designed for zero downtime and zero congestion. It makes it possible to spin up “AI micro-clouds where they are precisely needed” near to a factory or hospital and both on demand and instantaneously. The data being analyzed can stay local, with no need to send it a latency-extending centralized cloud. This can save time and money while also providing, if desired, fine-grained control over data sovereignty, locality, and compliance.
Vapor IO VEM-150 datacenter
Vapor IO says customers can operate at a low level with bare-metal instances, or they can use prepackaged third-party AI models and systems for specific use cases, including those for smart retail, smart manufacturing, and smart cities.
VAST Data is providing a data storage layer, a data lake, to Vapor IO’s Zero Gap AI service, with the joint offering “combining data capture, training, and inferencing at the edge.” The VAST-Vapor offering can “ perform AI at the edge in collaboration with AI performed in the centralized cloud, on-premises or in colocation datacenters,” according to VAST.
Crawford stated: “With VAST Data, we’re supercharging our Zero Gap AI platform, simplifying the deployment of AI pipelines and integrating the capabilities for these pipelines to asynchronously train, tune, inference, and retrain. This enhancement drives unprecedented value for our customers, making AI operations more dynamic and efficient than ever before.”
The two suppliers say customers can train models in a centralized datacenter or the cloud while seamlessly deploying real-time inferencing of that model at the edge, using any new data that is collected to improve the larger model continuously.
John Mao, Technical Alliances VP at VAST Data, said: “The proliferation of data requires an agile system that can span on-premises, near-premises, cloud and multi-cloud environments. The combination of these two products makes an ideal solution for enterprises looking to rapidly and cost-effectively scale their use of AI.”
Two-storey Vapor IO VEM-180 datacenter
As the combined VAST-Vapor technology rolls out across the US, the companies also have plans for expansion into global markets, taking advantage of VAST’s global namespace.
Neeloy Bhattacharyya, Director of Solutions Engineering for HPC/AI at VAST Data, blogged in April that VAST “provides a high-performance repository for customer data in each Zero Gap city. VAST provides multiple ways to get data on the platform ranging from traditional file and object interfaces to more modern approaches like streaming data directly by using Kafka.
“Next, it enables that data to be rapidly used to update a vector database that the AI model can in turn search very efficiently. Lastly, the VAST DataSpace ensures that all locations have access to the same data without having to constantly replicate it. This means customers who have deployed RAG-enabled AI models on Zero Gap can provide responses based on data gathered across the country without slowing down the model.”
AI Model serving dimensions diagram from Bhattacharyya’s blog
He notes that by “VAST has integrations with tools like Apache Spark that enable users to perform table scans or generate projections from billions of rows within seconds. By leveraging the VAST DataBase to store prompt, response, user, model, vector DB and many other details, users of the Zero Gap AI platform enjoy full traceability of all AI operations.”
Vapor IO has also partnered with IBM and Comcast. IBM’s Hybrid Cloud Mesh can operate on top of the Kinetic Grid to provide interoperability between Vapor IO’s micro datacenters, with a proof of concept demo showing the Atlanta and Chicago datacenters so linked. Comcast is one of Vapor IO’s early edge network partners to link its colos with customer edge premises.
Check out Vapor IO VEM datacenter datasheets here.
