Home Blog Page 29

Quantum wants to claw its way back to profitability – and growth

By
Chris Mellor
-

Analysis. Quantum has been wading through the mire. After two reverse stock splits, a hyperscaler tape system buying stop, a second set of financial reporting problems, and an abrupt end to CEO Jamie Lerner’s run of six revenue-raising quarters, the business is down in the dumps again. 

Quarterly revenues have dropped to their lowest level for more than 13 years. Back in 2011 Quantum was a $160 million-plus a quarter supplier, but it has steadily dropped since then. Jamie Lerner became the CEO in 2018 and arrested the revenue decline for a few quarters, before it resumed in 2020 to a $73.1 million low point in FY2021’s first quarter.

He clawed it back, over ten quarters, to $112 million in the third FY2023 quarter – but then hyperscaler customers started to stop buying Quantum’s Scaler tape libraries as Quantum would not lower the price below its own profitability level, and revenues collapsed. They were $105.3 million the next quarter, then $92.5 million and then vanished in puffs of financial reporting smoke. Quantum found it necessary to recalculate and restate its accounts in a massive snafu that caused its stock price to fall, making a reverse stock split necessary to retain its Nasdaq listing.

Its latest quarter, Q2 FY2025, saw $70.5 million in revenues, with a $13.5 million loss. It’s carrying $133 million in total debt. It last made a profit four years ago, in FY2020: $4.7 million in Q3. The last profit before that was $12.9 million in Q4 FY2015. This is a persistently loss-making concern that has been in a recovery state ever since Lerner became CEO.

It’s ironic that Lerner’s climb out of revenue misery was partially fuelled by selling tape library systems – the old, old technology that Quantum has not been able either to profit from, like Spectra Logic, or leave behind as it grew new product revenues.

Taking a cold look at its non-tape products, we see that none of them are leaders in their market niches. Or, if they are, the niche seems too small to sustain the whole enterprise. For example, we might say:

  • StorNext has great features, buttressed by allied ActiveScale and CatDV media asset management systems, but it appears it cannot support or grow Quantum on its own.
  • The DXi deduping backup appliance line lags behind Dell’s PowerProtect and the ExaGrid products.
  • The ActiveScale object storage line is another object storage product.
  • The USP video surveillance products, however well they’re doing, cannot rescue Quantum either.

Its latest technology – the Myriad storage operating system – is entering a crowded market and will need continued investment to make progress before it starts to generate a return.

Chris Evans.

Storage Architect consultant Chris Evans tells us: “I agree that the company needs some kind of drastic intervention. Quantum is definitely one of many companies selling products that still have some legacy value to a niche set of customers, yet at the same time, the revenue from them is not enough to invest in R&D to move them on to better solutions. Quantum is stuck in a scenario of multiple issues: 

  • The demand for its existing products is waning.
  • There’s no money for R&D to make them better (and little justification for improvement).
  • There is little or no asset value to the IP.
  • The money required for a pivot is more than the business justifies.

His conclusion: “It’s an interesting dilemma that has no obvious solution.”

Blocks & Files asked Quantum CEO Jamie Lerner if Quantum could ever return to the $150 million-plus revenue quarters of 2011? He provided a detailed and open answer.

Jamie Lerner.

“I think we can, but I have to walk you through what we’ve had to do when I came here. We had $225 million in debt. We had an SEC investigation for a lot of practices that weren’t good. We had a very aged product portfolio that had not been invested in, and a lot of aging infrastructure. And when I looked at that, I made the bet then, and I still think it’s the right bet, that the only way we could get to that $150 million plus quarters again – there’s no fancy marketing that could do that, there was no financial trickery – was going to take engineering execution. We were simply going to have to innovate, create things that either matched or leapfrogged our competition.

“But we hadn’t done that in a long time. I couldn’t just say: ‘Hey, let’s go build Myriad.’ We had to start with walking a walk.

“We had to just take StorNext to all-flash. We had to build user interfaces that were modern. So we had to start looking at StorNext and ActiveScale. We had to fill in holes we didn’t have, like  object storage. We had to learn about the cloud. We had to figure out how to do cloud-based analytics and the platform we built in the cloud. We had to learn about data services, sync, rep, compression, dedupe, inline compression and dedupe – things that we just had to get better at. We had to acquire things like better metadata management. 

“Now during that period, COVID-19 hit and wiped out our Media and Entertainment business, and I had to take more loans. Then we had an extremely unfortunate supply chain crisis, especially as it related to tape drives. Tough again. 

“Then, who would have ever believed our auditor would have to quit, and the new auditor says: ‘Redo your financials for ten million.’ And the result after we redid them? Effectively the same. 

“And so this reorganization of the company; what we’ve had to do has taken us financially to the very edge. I’ve had to build a new ERP system. Ours was 25 years old. We had to do it with our buildings and laboratories. The air conditioning, light was failing, and I had to build that new laboratory that you visited. 

“But now, if you look at us today, after taking us to the very edge, we have a new ERP system, we have a new laboratory. We have an entirely new product portfolio that we’re now reducing really into Myriad and ActiveScale, right? That’s been the whole reducing it down so that we have a very high speed file system and then a flash disk and tape data lake. 

“And I think there’s elements of these that are architecturally very unique. And it’s taken us to the brink, but now I’ve got a fresh portfolio, including the i7 Raptor, including Myriad, and with Myriad, we’re going to be announcing GPU direct. We’re going to be announcing a parallel client. We’re going to be announcing much more scale.

“ActiveScale runs on flash; it runs on disk; it runs two dimensionally, erasure coded tape. The i7 Raptor is shipping this month, and we already have multi-million dollar orders from cloud companies and enterprises. 

“So you look at this: after taking us to the brink, we have resolved the financial problems and these restatements to the point were so clean now, it’s ridiculous. We have new labs, new systems, a whole new product portfolio. 

“And what I like about this most recent quarter, we said, our negative EBITDA days are behind us. We’ve now done the cost-cutting we had to do because of all this spending. We’re now break-even – and going forward, we’re generating cash that we are going to use to start cranking up the sales and marketing effort. 

“So yes, it’s been a long and difficult road, but I think most people are shocked that, with our situation, we’ve been able to do this kind of innovation, build these kind of products, without hundreds of millions of dollars of venture capital investment, without the infrastructure of a Dell or Hewlett Packard. We’ve been able to do this, and it’s been small teams of very, very advanced engineers who’ve been very efficient and been able to build these products like Myriad.

“So I think we’re poised to begin that clawback. I think we’ve gone through all this shit, and it’s been a lot of shit that we’ve had to slog through, but we’ve slogged through it, and I think we’re coming out the other side.”

He continued: “It’s been the hardest six years of my life. It’s been the hardest thing I’ve ever taken on and what I just told you is the honest, just the damn honest truth of what we’ve been slogging through. And I’m only doing it because I do think in the end, it’ll be worth it.”

I asked a question about whether Quantum will be better taken into private ownership?

Lerner replied: “That’s a fair question. Doing this as a public company is the hardest way to do this, right? I’m putting out [SEC] 8Ks and having to do all these filings that customers go: ‘My God, what is this?’ And every time I re-organize the debt, it’s visbile. And every piece of our financial structure is visible and confusing. And the way we deal with adjusted EBITDA … there’s such technical accounting. 

“The other thing is, we are fragile, right? I mean, one or two 18-wheelers don’t ship, and I miss my revenue. Like, does it matter? I mean, to a public company that matters. To a private company, you’d be like, ‘Okay, I didn’t get the revenue in June. I got it in July.’ But to a public company that could be crisis. So I definitely think that many CEOs do this kind of work privately, the kind of surgery we’re doing, and I don’t think that’s off the table. 

“We’re looking at every way to get this completed, but I think it’s a fair question, and I do think this kind of work is easier when you’re you’re private.”

We asked: “Would it be equally fair to suggest that you already accomplished what you would have done if you were privately owned four or five years ago?” 

Lerner agreed: “I think that’s a fair statement. I mean, at this point we’ve done all the slogging. We’ve dragged our investors through all this muck already – you know, we’ve kind of gone through it – so, you know, it may be water under the bridge.”

It most probably is water under the bridge. Let’s wish Quantum freedom from any more accounting snafus, pandemics and supply chain hiccups and let it get on with selling its refreshed product portfolio.

Scaling the AI frontier and powering the future of innovation

By
Richard Greenwalt, Dell Technologies
-

COMMISSIONED: What’s holding back the next big breakthrough in AI – processing power or the infrastructure supporting it? The short answer is: both.

But having the right infrastructure is the real game-changer. While AI’s progress relies heavily on advanced algorithms and processing units, it’s the infrastructure supporting them that ultimately unlocks their full potential. Processing power is essential – without GPUs and high-performance computing systems like NVIDIA DGX SuperPOD, AI models wouldn’t be able to train, infer, or generate results at the speed required by today’s demands. But as models grow in complexity, generating massive amounts of data, the true bottleneck often lies in the ability to store, access, and move that data efficiently. Without robust, scalable storage, even the most powerful processors can be held back, waiting for data to arrive or be written.

This is where modern storage solutions like Dell PowerScale stand out from the competition. Certified for use with NVIDIA DGX SuperPOD, PowerScale provides the high-performance storage infrastructure necessary to keep up with the processing power of modern AI systems. By delivering scalable, efficient data handling, this high-performing storage solution ensures that AI applications can run at full throttle – no bottlenecks, no slowdowns. This certification marks a turning point for businesses aiming to harness the true potential of AI, offering them the infrastructure needed to drive transformative outcomes. Let’s explore how this synergy between processing power and storage infrastructure is enabling the future of AI innovation.

A new era of creativity and problem-solving

Generative AI has rapidly emerged as a game-changer across industries. Unlike traditional AI, which is primarily designed to recognize patterns and make decisions based on existing data, generative AI has the unique ability to create. This means it can produce original content – whether it’s images, text, music, or even complex models for scientific research. Think of tools like ChatGPT, DALL·E, or MidJourney. They’re built on generative AI models, and their capacity for innovation is virtually limitless.

What makes generative AI particularly fascinating is its potential to disrupt a variety of fields. In healthcare, for instance, AI models can generate new drug compounds, expediting the process of medical discovery. In entertainment, AI-generated scripts, graphics, and music open the door to creative possibilities previously unimaginable. Finance can leverage generative models to simulate economic scenarios, while manufacturing industries benefit from AI’s ability to generate optimized designs for products and workflows.

However, this leap in capability requires one thing in abundance – data. To function effectively, generative AI models must learn from vast amounts of data, which can range from high-resolution images to detailed financial records. As a result, the demand for scalable, high-performance storage solutions capable of handling these massive workloads has surged.

Dell Technologies has long been at the forefront of creating cutting-edge solutions designed to address the evolving needs of businesses. Earlier this year, Dell made a significant announcement: PowerScale, its industry-leading storage platform, became the world’s first Ethernet-based storage certified for NVIDIA DGX SuperPOD. This milestone is more than just a certification – it’s a recognition of Dell’s commitment to building the infrastructure that drives tomorrow’s AI breakthroughs.

But why is this certification important? NVIDIA DGX SuperPOD is a powerful platform designed to enable the rapid development and deployment of AI models at scale. By integrating PowerScale, enterprises can achieve faster, more efficient access to data, which is crucial for AI workloads. With this certification, Dell offers organizations a storage solution that is not only robust and scalable but also perfectly optimized to work with NVIDIA’s AI systems.

As Martin Glynn, Senior Director of Product Management at Dell Technologies, aptly puts it: “The world’s first Ethernet storage certification for NVIDIA DGX SuperPOD with Dell PowerScale combines Dell’s industry-leading storage and NVIDIA’s AI supercomputing systems, empowering organizations to unlock AI’s full potential, drive breakthroughs, and achieve the seemingly impossible.”

PowerScale certification: What it means for AI infrastructure

This collaboration between Dell and NVIDIA is set to revolutionize how businesses approach AI, providing them with the tools they need to manage the massive datasets that power AI-driven innovation.

Dell PowerScale’s certification for NVIDIA DGX SuperPOD isn’t just a stamp of approval; it’s a testament to the platform’s extensive capabilities. Let’s break down some key features that make PowerScale the ideal storage solution for organizations looking to harness the power of AI:

– Enhanced network access: Dell PowerScale takes advantage of NVIDIA Magnum IO, GPUDirect Storage, and NFS over RDMA (Remote Direct Memory Access), which are integrated with NVIDIA ConnectX NICs and Spectrum switches. These technologies work together to accelerate data access and minimize latency. For AI models, where speed is critical, this means faster data transfer and, consequently, faster training, checkpointing, and inferencing tasks.

– Maximized performance: One of the new features of Dell PowerScale is its Multipath Client Driver, a technology designed to optimize data throughput and maximize performance. This is crucial when working with large AI models that require high-speed data transfer. In addition, Dell PowerScale exceeds the performance thresholds across all NVIDIA DGX SuperPOD performance tiers – Good, Better, and Best – by utilizing Ethernet connections. This ensures IT departments can confidently rely on PowerScale’s robust file storage capabilities for optimal GenAI workload performance.

– Seamless scalability: AI workloads are known for their ever-increasing demands on storage. As datasets grow larger and more complex, organizations need a storage system that can scale effortlessly. PowerScale allows businesses to add nodes as needed, ensuring that they can grow their storage infrastructure in tandem with their AI applications. This scalability makes PowerScale particularly appealing for industries like media and entertainment, where storage demands can skyrocket as AI models evolve.

– Federal-grade security: Data security is a top priority in today’s world, especially when dealing with sensitive information. Dell PowerScale meets the highest security standards, including certification by the U.S. Department of Defense. This level of security makes PowerScale an ideal solution for mission-critical applications, where data protection is paramount.

– Operational efficiency: PowerScale’s architecture is built to optimize both performance and efficiency. By minimizing power consumption and operational costs, it helps businesses reduce their environmental impact while maintaining top-tier performance. This balance of power and efficiency is a key reason why PowerScale stands out as a leader in the storage space.

Empowering the future of AI innovation

The certification of Dell PowerScale as the first Ethernet-based storage for NVIDIA DGX SuperPOD is a pivotal moment in the evolution of AI infrastructure. By combining Dell’s expertise in storage solutions with NVIDIA’s AI leadership, organizations now have a clear path to scaling their AI initiatives without sacrificing performance or flexibility.

As Tony Paikeday, Senior Director of AI Systems at NVIDIA, points out, “To harness the transformative power of generative AI, organizations are seeking robust and scalable infrastructure that can help unlock insights and drive innovation.” PowerScale’s certification ensures that businesses can meet these demands, empowering them to embrace the future of AI with confidence.

As AI continues to evolve, its potential to drive business transformation will only grow. Generative AI, in particular, represents a new frontier of creativity and problem-solving, and with the right infrastructure, organizations can tap into this power to unlock new levels of innovation.

Dell PowerScale’s certification for NVIDIA DGX SuperPOD isn’t just a technical achievement; it’s the key to unlocking AI’s full potential. Businesses that adopt this technology will not only stay ahead of the curve but also pave the way for breakthroughs that were once unimaginable. AI’s future is limitless, but the right tools and infrastructure are essential to driving true transformation and sustained growth.

For more information, visit Dell PowerScale.

Brought to you by Dell Technologies.

WEKA unveils WARRP reference architecture and previews Nvidia Grace-powered storage cluster

By
Chris Mellor
-

WEKA is previewing its parallel file system working on a storage server cluster powered by Nvidia’s Grace Superchips, and has developed its WEKA AI RAG Reference Platform (WARRP) reference architecture to speed GenAI inferencing development.

The cluster features WEKA’s Data Platform software running on Supermicro storage servers using Grace Superchips. This software has a distributed architecture and kernel-bypass technology.

WEKA says Nvidia’s Grace integrates the level of performance offered by a flagship x86-64 two-socket workstation or server platform into a single module. Grace Superchips have two Grace CPUs, each with 72 x Arm Neoverse V2 cores, connected with a Scalable Coherency Fabric (SCF), that delivers 3.2 TBps of bisection bandwidth, and high-speed LPDDR5X memory that delivers up to 500 GBps of bandwidth. The Grace CPU servers uses BlueField-3 DPUs, also Arm-powered and with RDMA/RoCE acceleration, to offload networking and other tasks from the main Grace CPUs. ConnectX-7 inter-server networking is used within the cluster.

The main benefit is power efficiency, with WEKA saying the Grace CPU superchip delivers the performance of a dual-socket x86 CPU server at half the power. But customers don’t forego speed as its software “combined with Grace CPUs’ LPDDR5X memory architecture, ensures up to 1 TBps of memory bandwidth and seamless data flow, eliminating bottlenecks.” 

Nilesh Patel, WEKA
Nilesh Patel

WEKA’s chief product officer, Nilesh Patel, stated: “AI is transforming how enterprises around the world innovate, create, and operate, but the sharp increase in its adoption has drastically increased data center energy consumption, which is expected to double by 2026, according to the International Atomic Energy Agency.”

Altogether, customers “can achieve faster AI model training, reduced epoch times, and higher inference speeds, making it the ideal solution for scaling AI workloads efficiently.”

Patrick Chiu, Supermicro’s Senior Director for Storage Product Management, said: “The system design features 16 high-performance Gen5 E3.S NVMe SSD bays along with three PCIe Gen 5 networking slots, which support up to two Nvidia ConnectX-7 or BlueField-3 SuperNIC networking adapters and one OCP 3.0 network adapter. The system is ideal for high-performance storage workloads like AI, data analytics, and hyperscale cloud applications.”

WEKA claims that, through data copy reduction and cloud elasticity, its software can shrink data infrastructure footprints by 4-7x and reduce carbon output – avoiding up to 260 tons of CO2e per PB stored annually and lowering energy costs by 10x. 

WARRP is a design blueprint for the development of an inferencing infrastructure framework incorporating retrieval-augmented generation (RAG), whereby large language models (LLMs) can gather new data from external sources. WEKA says “using RAG in the inferencing process can help reduce AI model hallucinations and improve output accuracy, reliability and richness, reducing the need for costly retraining cycles.”

WARRP is based on WEKA’s Data Platform software allied to Nvidia’s NIM microservices and NeMo retriever, AI workload and GPU orchestration capabilities from Run:ai, Kubernetes for data orchestration, and Milvus Vector DB for data ingestion. This has similarities to Pure Storage’s GenAI Pod, which was also announced at SC24. WARRP is hardware, software, and cloud-agnostic.

Run:ai CTO Ronen Dar said: “The WARRP reference architecture provides an excellent solution for customers building an inference environment, providing an essential blueprint to help them develop quickly, flexibly and securely using industry-leading components from Nvidia, WEKA and Run:ai to maximize GPU utilization across private, public and hybrid cloud environments.” 

The first release of the WARRP reference architecture is now available to download here. The WEKA and Supermicro Grace CPU Superchip storage system will be commercially available in early 2025. SC24 attendees can visit WEKA in Booth #1931 for more details and a demo of the system.

Hitachi Vantara adds Nvidia HGX GPUs to iQ framework

By
Chris Mellor
-

Hitachi Vantara is making Nvidia’s HGX part of its IQ GenAI infrastructure range.

The main storage system suppliers are all scrambling to provide customers with reference architecture-type frameworks that place their storage inside a GenAI inferencing system, typically including Nvidia GPUs, NIM And MeMo retriever microservices, some Kubernetes orchestration, and large language model (LLM) sources such as Hugging Face. Pure Storage’s GenAI Pod, WEKA’s WARRP, and Hitachi iQ are three examples.

Jason Hardy, Hitachi Vantara
Jason Hardy

Jason Hardy, Hitachi Vantara’s CTO for AI, said: “The Hitachi iQ offering with Nvidia HGX is the latest example of how we are at the forefront of AI innovation. As one of the only vendors to offer a complete, end-to-end AI infrastructure solution, we marry world-class technology with deep industry expertise for a powerful, one-stop-shop experience.”

Nvidia DGX offerings are fixed and integrated GPU systems supported directly by Nvidia. Its HGX products are modular building blocks for OEMs to use when building their GPU servers, with tailored features such as the number of GPUs, CPU types, memory, storage, and networking components to meet their own requirements. 

Hitachi Vantara quotes ESG research finding that 97 percent of organizations view GenAI as a top-five priority, which explains the popularity of storage suppliers wrapping GenAI frameworks around their products. Its Hitachi iQ offering with Nvidia HGX combines storage, networking, servers with Nvidia H100 and H200 Tensor Core GPU options, and the Nvidia AI Enterprise end-to-end, cloud-native software platform – including NIM and NeMo – for the development and deployment of GenAI applications.

The storage element is the Hitachi Content Software for File (HCFS) platform, based on the WEKA Data Platform, with integrated object storage. Using this provides “a zero-copy architecture that eliminates wasteful data copying and transfer times between storage silos for different phases of AI.”

Hitachi iQ also features AMD EPYC processors, PCIe Gen 5 E3.S NVMe SSDs, high-performance networking with Nvidia ConnectX-7 400 Gbps InfiniBand or Ethernet NICs.

Hitachi iQ with the Nvidia HGX platform and Hitachi Content Software for File are now available globally. There is more information about Hitachi iQ here.

DDN supplying storage for xAI’s Colossus supercomputer

By
Chris Mellor
-

DDN storage is being used in an expansion phase of Elon Musk’s xAI Colossus supercomputer.

Grok is xAI’s name for its large language model, while Colossus is the GPU server-based supercomputer used to train it and run inferencing tasks. The Colossus system is based in a set of datacenter halls in Memphis, Tennessee. The Grok LLM is available for use by X/Twitter subscribers and competes with OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and Meta’s LLaMA.

DDN is supplying its EXAScaler and Infinia systems, with EXAScaler being a Lustre parallel access file system layered on scale-out all-flash and hybrid hardware and Infinia being DDN’s petabyte-scale object storage system, typically using all-flash nodes.

Alex Bouzari, CEO and co-founder of DDN, stated: “Our solutions are specifically engineered to drive efficiency at massive scale, and this deployment at xAI perfectly demonstrates the capabilities of our high-performance, AI-optimized technology.” 

Alex Bouzari, DDN
Alex Bouzari

Elon Musk, xAI CEO, not specifically referencing DDN, said on X: “Colossus is the most powerful AI training system in the world. Moreover, it will double in size to 200k (50k H200s) in a few months. Excellent work by the team, Nvidia and our many partners/suppliers.” 

Dion Harris, Nvidia’s director of accelerated datacenter product solutions, did name DDN, however, saying: “Complementing the power of 100,000 Nvidia Hopper GPUs connected via the Nvidia Spectrum-X Ethernet platform, DDN’s cutting-edge data solutions provide xAI with the tools and infrastructure needed to drive AI development at exceptional scale and efficiency, helping push the limits of what’s possible in AI.” 

There have been several iterations of xAI’s Grok:

  • Grok 1 is a 314-billion-parameter Mixture-of-Experts model, announced in March 2024 and trained from scratch by xAI.
  • Grok 1.5 was announced later in March 2024 as an updated version with improved reasoning capabilities and an extended context length of 128,000 tokens.
  • Grok 2 was announced in August 2024 and used 20,000 Nvidia GPUs for training. There were two models: Grok-2 and Grok-2 mini.
  • Grok 3 phase 1 has 100,000 Hopper H100 GPUs and Nvidia Spectrum-X Ethernet. Its development was announced in July 2024 with availability slated for December.
  • Grok 3 phase 2 will scale to 200,000 GPUs by adding 100,000 more Hopper GPUs (including 50,000 H200s).
  • Grok 3 phase 3 could scale to 300,000 GPUs with 100,000 more Blackwell B200 GPUs according to a Musk prediction.

The entire array of systems is connected over a single Ethernet-based RDMA fabric and is possibly the largest GenAI cluster in the world to date. Grok 3 phase 1 was built in 122 days and took 19 days to go from first deployment to training.

VAST announced it was the storage behind Grok 3 phase 1, saying it’s “honored to be the data platform behind xAI’s Colossus cluster in Memphis, fueling the data processing and model training that powers this groundbreaking initiative. Colossus, featuring over 100,000 Nvidia GPUs.” Jeff Denworth, VAST Data co-founder, said: “I’m proud to say that VAST is the tech that is used primarily by this amazing customer.” Note that “primarily” means not the only supplier. The VAST Data storage nodes are shown in a video about the Colossus datacenter’s scale.

DDN president Paul Bloch said in LinkedIn post in late October that DDN was involved in Grok 3 phase 1 as the “primary and main data intelligence and storage platforms provider.”

This raised questions about the relative storage roles DDN and VAST Data played in the Grok 3 phase 1 Colossus system as both are using the “primary” word. When he was asked about this statement, Denworth couldn’t comment on the role of DDN’s storage, only telling us this about xAI and phase 1 of Grok 3: “They’re training and checkpointing and storing their data on VAST.” 

Now DDN is involved as a storage supplier for Grok 3 phase 2. Asked about how it related to VAST Data in Colossus, a DDN spokesperson told us: “As for VAST Data, we can’t comment on their current role, but what we do know is that DDN’s cutting-edge technology and close collaboration with Nvidia have been key to moving Colossus forward. Our solutions are designed to meet the toughest data challenges, helping organizations like xAI stay ahead in the AI race.”

Perhaps xAI is using Grok 3 phase 1 and Grok 3 phase 2 to support different workloads that need different storage data supply characteristics. In terms of public flagship AI and hyperscaler customer wins, both VAST and DDN can quote xAI’s Colossus with Hammerspace being used by Meta. WEKA customers include Midjourney and Stability AI. All four have convinced their customers that they can provide the performance, scale, reliability, power efficiency, and cost needed.

Pure Storage’s FlashBlade//S500 gains Nvidia SuperPOD certification

By
Chris Mellor
-

Pure Storage has announced certification of its FlashBlade//S500 storage with Nvidia’s DGX SuperPOD along with a new GenAI Pod system for inferencing.

The FlashBlade//S500 is a high-end, performance-optimized FlashBlade system, with the mid-range S200 and entry-level S100 completing the //S range. The GenAI Pod is a “full-stack solution providing turnkey designs” based on Pure Storage arrays. SuperPOD is Nvidia’s largest GPU server and there were six certified SuperPOD storage suppliers: DDN and its A³I AI400X2T Lustre array, Dell with PowerScale using Ethernet storage, IBM with Storage Scale System 6000, NetApp with EF600 running BeeGFS, VAST Data with its Data Platform, and WEKA. Pure Storage’s FlashBlade//S500 with Ethernet is now the seventh SuperPOD storage supplier.

Pure Storage FlashBlade//S
Pure Storage FlashBlade//S
Tony Paikeday, Nvidia
Tony Paikeday

Nvidia’s Tony Paikeday, Senior Director of AI Systems, stated: “The Pure Storage GenAI Pod with Nvidia AI Enterprise software and the certification of FlashBlade//S500 with Nvidia DGX SuperPOD can help organizations eliminate infrastructure complexity, speed deployments, and simplify operations.”

Pure’s GenAI Pod includes validated designs that enable turnkey solutions for GenAI use cases, such as drug discovery, trade research and investment analysis, and RAG with agentic frameworks for semantic search, knowledge management, and chatbots. It integrates Pure’s Portworx with its Kubernetes operations, container storage, and backup software facilities. GenAI Pod has services that provide automated deployments of Nvidia’s NeMo and NIM microservices and the Milvus vector database, with, Pure says, a one-click deployment and streamlined Day 2 operations for vector databases and foundation models.

The full-stack aspect means the GenAI Pod includes hardware, software, foundational models, and professional services from industry AI vendors: Arista, Cisco, KX, Meta, Nvidia, Red Hat, Supermicro, and WWT.

Pure added Pure Fusion for Files, Zero Move Tiering, and Real-time Enterprise File services with always-on multi-protocol, along with auditing, QoS, SMT for file, and an AI Copilot to FlashBlade in September. It also added 150 TB flash drive (Direct Flash Module) support then as well.

Pure’s AIRI//S AI infrastructure storage system, which uses FlashBlade//S storage, has Nvidia BasePOD certification. Up until now, Pure has not had SuperPOD certification, putting it at a disadvantage in bids where data is served to SuperPODs.

Wedbush financial analyst Matt Bryson commented: “The GenAI Pod may have more significant longer-term ramifications for Pure Storage if we see increased enterprise adoption of AI hardware (perhaps as inference applications take off) and Pure’s solution enables easier/better enterprise AI implementations using Pure hardware. However, in the near term, we believe finally achieving SuperPOD certification might be the more important accomplishment, given Pure can finally point to checking that box during competitive bake-offs.”

Pure says the new GenAI validated designs, along with FlashBlade//S500 SuperPOD certification, expand upon its AI system portfolio, including AIRI with Nvidia DGX BasePOD, validated Nvidia OVX servers, and FlashStack for AI with Cisco. 

The Pure Storage GenAI Pod is expected to be generally available in the first half of 2025.

DDN boosts A³I IO speeds and IOPS for Nvidia AI workloads

By
Chris Mellor
-

DDN announced a fourth generation of the A³I (Accelerated, Any-Scale AI) system at SC24, increasing both its sequential data access and random read IOPS.

The A³I systems are storage systems focused on supporting Nvidia GPUs processing AI workloads, and support Nvidia’s GPU servers, such as the DGX A100, DGX Pod, and DGX SuperPod. DDN bases them on its SFA (Storage Fusion Architecture) EXAScaler storage arrays running Lustre parallel file system software. There have been three generations with each new generation increasing the IO speed, but not, until now, the IOPS rating. A fourth generation is being announced, the AI400X3T system, built in close collaboration with Nvidia, according to DDN, and this increases both the IO speed and the maximum number of IOPS.

Sven Oehme, DDN
Sven Oehme

DDN chief technology officer Sven Oehme said: “At DDN, we’re all about tearing down the roadblocks that companies hit when they try to scale AI and HPC.”

The existing AI400X2 and AI200X2 options now support denser disk enclosures, reducing costs per petabyte and preserving valuable datacenter space. The AI200X2 appliance scales up to 20 PB per rack and the AI400X2 handles up to five QLC appliances per rack. The underlying EXAScaler storage system introduces Client-Side Compression, which reduces data size without impacting performance, overcoming competing server-side compression performance issues.

The EXAScaler software now features native multi-tenancy, allowing secure data segregation for cloud providers and multi-user enterprise environments. The EXAScaler Management Framework (EMF) provides improved monitoring and health reporting tools.

The AI400 generations have developed like this:

AI400X all-flash array

  • Up to 48 GBps read
  • Up to 34 GBps write
  • 3 million IOPS
  • 8 x EDR/HDR100 InfiniBand or 100GbE
  • PCIe gen 3

    AI400X2 all-flash array

    • Up to 90 GBps read
    • Up to 65 GBps write
    • 3 million IOPS
    • 8 x HDR InfiniBand or 100/200GbE
    • PCIe gen 4

    The AI400X2 supports up to 5 PB of QLC flash per rack.

    AI400X2T – all-flash Turbo appliances

    • Up to 120 GBps read
    • Up to 75 GBps write
    • 3 million IOPS

    A single AI400X2T delivers 47 GBps read and 43 GBps write bandwidth to a single HGX H100 GPU server. Each AI400X2T appliance delivers over 110 GBps and 3 million IOPS directly to HGX H100 systems. 

    AI400X3 – based on SFA400X3 all-flash array with 2 x AMD Genoa CPUs

    • Up to 145 GBps read
    • Up to 95-116 GBps write
    • 1.5/5 million IOPS (48/64 core SKUs)
    • 4x QSFP112 InfiniBand NDR/400GbE (Nvidia BlueField-3 SuperNIC, for Spectrum-X) 
    • 4x OSFP InfiniBand NDR/400GbE 
    • 8x QSFP112 InfiniBand NDR200/200GbE
    DDN AI400 generations

    The SFA400X3 has NVMe-oF and SAS expansion coming in early 2025 to increase its scalability:

    • NVMe-oF SE2420, 24-Bay Enclosures (2025 Q2 target) 
    • SAS4 SS9024, 90-Bay Enclosures (2025 Q4 target)

    Get more background AI400X3 information here.

    Cloudian integrates Nvidia GPUDirect acceleration for object storage

    By
    Chris Mellor
    -

    Cloudian announced its integration with Nvidia Magnum IO GPUDirect storage technology, using RDMA parallel transfer, and putting file and object storage data on an equal footing in serving data to Nvidia GPUs.

    This “simplifies the management of AI training and inference datasets – at petabyte and exabyte scales – while reducing costs by eliminating the need for complex data migrations and legacy file storage layers.”

    Michael Tso, Cloudian
    Michael Tso

    Cloudian CEO Michael Tso said of the move: “For too long, AI users have been saddled with the unnecessary complexity and performance bottlenecks of legacy storage solutions. With GPUDirect Storage integration, we are enabling AI workflows to directly leverage a simply scalable storage architecture so organizations can unleash the full potential of their data.”

    Rob Davis, Nvidia VP of storage technology, said: “Fast, consistent, and scalable performance in object storage systems is crucial for AI workflows. It enables real-time processing and decision-making, which are essential for applications like fraud detection and personalized recommendations.”

    GPUDirect, up until now, has been an Nvidia file-focused protocol that eliminates needless file data movement within a server. Normally a storage server CPU copies data from a storage drive into its memory and then authorizes its transmission over a network link into a GPU’s DRAM. The GPUDirect protocol enables a direct connection to be made between the storage drive and GPU memory, cutting out file movement into the storage server’s memory, lowering file data access latency and keeping GPUs busy rather than having them wait for file IO.

    Cloudian diagram
    Cloudian diagram

    However, GPUs still wait for object data IO, rendering the use of object data for GPU AI processing impractical. Cloudian and MinIO have broken this bottleneck, enabling stored object data to be used directly in AI processing rather than indirectly via migration into file data, which can then travel via GPUDirect to the GPUs.

    The HyperStore GPUDirect object transfer relies on Nvidia ConnectX and BlueField DPU networking technologies, enabling direct communication between Nvidia GPUs and multiple Cloudian storage nodes.

    Cloudian diagram
    Cloudian diagram. Object GPUDirect works like this: (1) Data requests are initiated via the S3 API, (2) Instead of routing through system memory and CPU, data moves directly to GPU memory. (3) RDMA enables parallel transfer from multiple Cloudian nodes

    Cloudian says its GPUDirect-supporting HyperStore delivers over 200 GBps from a single system with performance sustained over a 30-minute period without the use of data caching. It “slashes CPU overhead by 45 percent during data transfers.”

    There are no Linux kernel modifications, eliminating “the security exposure of vendor-specific kernel modifications.”

    CMO Jon Toor wrote in a blog post: “Cloudian has spent months collaborating closely with Nvidia to create GPUDirect for Object Storage. With GPUDirect for Object, data can now be consolidated on one high-performance object storage layer. The significance is simplification: a single source of truth that connects directly to their GPU infrastructure. This reduces cost and accelerates workflows by eliminating data migrations.”

    Cloudian HyperStore’s effectively limitless scalability enables AI data lakes to grow to exabyte levels, and its centralized management ensures “simple, unified control across multi-data center and multi-tenant environments.”

    The company says it accelerates AI data searches with integrated metadata support, allowing “easy tagging, classification, and indexing of large datasets.” It says file-based systems depend on “rigid directory structures and separate databases for metadata management.” With Cloudian and other object stores, metadata is natively handled within the object storage platform, “simplifying workflows and speeding up AI training and inference processes.”

    Michael McNerney, SVP of Marketing and Network Security at Supermicro, said: “Cloudian’s integration of Nvidia GPUDirect Storage with the HyperStore line of object storage appliances based on Supermicro systems – including the Hyper 2U and 1U servers, the high-density SuperStorage 90-bay storage servers, and the Simply Double 2U 24-bay storage servers – represents a significant innovation in the use of object storage for AI workloads. This will enable our mutual customers to deploy more powerful and cost-effective AI infrastructure at scale.”

    David Small, Group Technology Officer of Softsource vBridge, said: “Cloudian’s GPUDirect for Object Storage will simplify the entire AI data lifecycle, which could be the key to democratizing AI across various business sectors, allowing companies of all sizes to harness the power of their data. We’re particularly excited about how this could accelerate AI projects for our mid-market clients who have previously found enterprise AI solutions out of reach.”

    Nvidia is working with other object storage suppliers. Toor tells us: “GPUDirect is an Nvidia technology that we integrate with. We did foundational work on the code to support object, so we believe we are on the forefront. But like any industry standard, it will be driven by ecosystem growth. And that growth is a good thing. It will cement the position of object storage as an essential enabler for large-scale AI workflows.”

    MinIO announced its AIStor with GPUDirect-like S3 over RDMA earlier this month. Scality introduced faster object data transfer to GPUs wIth its Ring XP announcement in October.

    Cloudian HyperStore with Nvidia Magnum IO GPUDirect Storage technology is available now. To learn more, explore Cloudian’s views on GPUDirect in its latest blog post.

    Dell expands lakehouse and AI server range at SC24

    By
    Chris Mellor
    -

    Dell is extending its AI Factory range at the Supercomputing 2024 (SC24) event in Atlanta with Data Lakehouse and AI-focused server announcements.

    It says the Data Lakehouse updates provide modern architectures for efficiently managing and analyzing data for AI tasks. It will expand to include Apache Spark for distributed data processing at scale. This Spark integration “will provide significant efficiency gains, with a unified approach for data analytics, management, processing and analysis for faster, more actionable insights.”

    Arthur Lewis, president, Infrastructure Solutions Group, Dell Technologies, stated: “Getting AI up and running across a company can be a real challenge. We’re making it easier for our customers with new AI infrastructure, solutions and services.”

    Dell’s Data Lakehouse was announced back in March and featured the Starburst Trino query engine, Kubernetes-orchestrated lakehouse system software, and scale-out S3-compatible object storage based on Dell’s ECS, ObjectScale, or PowerScale storage products. The Starburst code included so-called Warp Speed technology, with Apache Lucene indexing and caching technology. 

    Apache Spark is a distributed data processing engine designed for batch processing, real-time streaming, machine learning, and advanced analytics. Starburst Trino is a distributed SQL query engine intended for interactive and ad hoc queries across heterogeneous data sources.

    The company boosted lakehouse query speed, added and upgraded connectors, and improved monitoring and security in June.

    Dell announced two new servers in 4RU chassis for its 19-inch IR5000 rack:

    • The liquid-cooled PowerEdge XE9685L has dual 5th Gen AMD EPYC CPUs paired with the Nvidia HGX H200 or B200 platforms, and up to 12 PCIe gen 5.0 slots. It has customizable configurations designed for AI, machine learning, high performance computing (HPC) and other data-intensive workloads. There can be up to 96 Nvidia GPUs per rack.
    • The air-cooled PowerEdge XE7740 has dual Intel Xeon 6 CPUs with P-cores and up to 8 double-wide accelerators, including Intel Gaudi 3 AI accelerators or Nvidia H200 NVL, or up to 16 single-wide accelerators, like the Nvidia L4 Tensor Core GPU. It is intended for GenAI model fine-tuning or inferencing and analyzing large datasets.

    Dell has a new XE server coming that will support Nvidia’s GB200 Grace Blackwell Superchip, with up to 144 GPUs per IR7000 rack (see bootnote).

    It is also introducing:

    • Dell AI Factory with Nvidia gets HGX H200 and H100NVL support options, providing up to 1.9x higher performance compared to the HGX H100.
    • Agentic RAG with Nvidia uses PowerEdge, PowerScale and Nvidia AI Enterprise software and GenAI tools including NeMo Retriever microservices and the Nvidia AI Blueprint for multimodal PDF data extraction.
    • Dell Validated Designs for AI PCs open source guides include NPU technology. Developers can customize the modular designs to integrate features like LLMs, vision, text and speech into applications.

    It says the Agentic RAG with Nvidia design “helps organizations with large datasets use AI agents to improve RAG workflow performance, handle complex queries, and deliver higher quality outcomes.”

    Dell’s Professional Services can, it says, ease AI adoption with Advisory and Implementation Services for Sustainable Data Centers, Data Management Services, Design Services for AI Networking, and Implementation Services for ServiceNow Now Assist.

    Availability

    • The PowerEdge XE9685L will be globally available Q1 calendar 2025.
    • The PowerEdge XE7740 will be globally available Q2 calendar 2025.
    • Updates to the Dell Data Lakehouse are globally available now.
    • Dell Validated Designs for AI PCs are globally available now. 
    • Dell Generative AI Solutions with Nvidia – GPU update will be available Q4 calendar 2024.
    • Dell Generative AI Solutions with Nvidia – Enterprise-scale RAG is globally available now. 
    • Dell Data Management Services are available in select countries now.
    • Dell Services for Sustainable Data Centers are available in select countries now.
    • Dell Design Services for AI Networking are available in select countries now.
    • Dell Implementation Services for ServiceNow Now Assist are available in select countries now.

    Read more in a server blog here and, on AI PC adoption, here.

    Bootnote

    The Dell Integrated Rack Scalable Systems (IRSS) program is a turnkey factory integration program that delivers fully loaded, plug-and-play rack-scale systems with Dell Smart Cooling. IRSS has one-call service and support options for the entire rack and Dell takes care of the packaging waste and recycling.

    The IR5000 is a standard 19-inch rack engineered for general-purpose datacenter applications. The IR7000 is a 50U standard rack incorporating liquid cooling, with the ability for near 100 percent heat capture, and has 21-inch wide sleds. This rack is for large-scale HPC and AI workloads requiring high power.

    AI processing speeds continue to improve in MLPerf Training

    By
    Antony Savvas
    -

    MLCommons has revealed new results for MLPerf Training v4.1, which the org says highlights “the strong alignment between the benchmark suite and the current direction of the AI industry.”

    MLCommons is an open engineering consortium and a leader in building benchmarks for AI data processing across different segments, including AI training. It says the MLPerf Training benchmark suite measures how fast systems can train models to a target quality metric.

    MLPerf Training v4.1 includes preview category submissions using new and upcoming hardware accelerators Google Trillium TPUv6 and Nvidia Blackwell B200.

    Hiwot Kassa, MLCommons
    Hiwot Kassa

    “As AI-targeted hardware rapidly advances, the value of the MLPerf Training benchmark becomes more important as an open, transparent forum for apples-to-apples comparisons,” said Hiwot Kassa, MLPerf Training working group co-chair. “Everyone benefits: vendors know where they stand versus their competitors, and their customers have better information as they procure AI training systems.”

    The benchmark suite comprises full system tests that stress models, software, and hardware, for a range of machine learning (ML) applications. The open source and peer-reviewed benchmark suite promises a level playing field for competition that “drives innovation, performance, and energy efficiency for the entire industry,” says MLCommons.

    The latest Training v4.1 results show a substantial shift in submissions for the three benchmarks that represent generative AI training workloads: GPT-3, Stable Diffusion, and Llama 2 70B LoRA, with a 46 percent increase in submissions in total across these three.

    The two newest benchmarks in the MLPerf Training suite, Llama 2 70B LoRA and Graph Neural Network (GNN), both had notably higher submission rates: a 16 percent increase for Llama 2, and a 55 percent increase for GNN. They both also saw significant performance improvements in the v4.1 round compared to v4.0, when they were first introduced. There was a 1.26x speedup in the best training time for Llama 2, and a 1.23x speedup for GNN.

    The Training v4.1 round includes 155 results from 17 submitting organizations, including ASUSTeK, Azure, Cisco, Clemson University Research Computing and Data, Dell, FlexAI, Fujitsu, GigaComputing, Google, Krai, Lambda, Lenovo, NVIDIA, Oracle, Quanta Cloud Technology, Supermicro, and Tiny Corp.

    David Kanter, MLCommons
    David Kanter

    “We would especially like to welcome first-time MLPerf Training submitters FlexAI and Lambda,” said David Kanter, head of MLPerf at MLCommons. “We are also very excited to see Dell’s first MLPerf Training results that include power measurement. As AI adoption skyrockets, it is critical to measure both the performance and the energy efficiency of AI training.”

    On the new accelerator competition between Google and Nvidia, Karl Freund, founder and principal analyst at Cambrian-AI Research, wrote: “The Google Trillium TPU delivered four results for clusters ranging from 512 to 3,072 Trillium accelerators. If I contrast their performance of 102 minutes for the 512-node cluster, it looks pretty good until you realize that the Nvidia Blackwell completes the task in just over 193 minutes using only 64 accelerators. When normalized, always a dangerous and inaccurate math exercise, that makes Blackwell over 4x faster on a per-accelerator comparison.”

    This may well illustrate that Nvidia is set to continue leading the AI processing market going forward.

    MLCommons was founded in 2018, and has over 125 industry members.

    Arcitecta and Wasabi team up to simplify cloud storage

    By
    Antony Savvas
    -

    Data orchestrator Arcitecta is partnering with Wasabi Technologies to allow organizations to integrate Wasabi’s “hot” cloud storage into their workflows.

    Arcitecta provides MediaFlux Universal Data System products covering file and object data storage and management software. This technology has a single namespace and tiering capability covering on-premises SSDs, disk and tape, and the public cloud, with a data mover and metadata database. While Arcitecta says integrating cloud storage into an organization’s workflow can offer many advantages, such as improved scalability, accessibility, and collaboration, it can be “challenging” with concerns around data security, compliance, migration, compatibility, performance, latency, and data governance.

    Data management across multiple environments presents more complexity. Storing data on-premises and in private or public clouds can create silos, making it difficult to have a unified view of the data. In addition, ensuring that data remains consistent and synchronized across different environments can become tricky, especially when updates occur simultaneously in multiple locations.

    Arcitecta diagram with Wasabi cloud in bottom right position
    Arcitecta diagram with Wasabi cloud in bottom right position

    Arcitecta says it can address these issues, claiming this will make it simpler for organizations to integrate Wasabi’s cloud storage into workflows. The Wasabi cloud appears like any other storage managed by Mediaflux, which acts as a gateway, allowing users to access Wasabi cloud storage and all their data, regardless of where it resides, through one unified view.

    Jason Lohrey, Arcitecta
    Jason Lohrey

    “Organizations need fast and easy access to their data and, for distributed workflows in particular, cloud storage is a great option,” said Jason Lohrey, CEO and founder of Arcitecta. “Our partnership with Wasabi enables customers to seamlessly store, manage, and access their data from Wasabi’s cloud into their day-to-day workflows, all within a single, unified view and namespace, to accelerate decision-making.”

    Mediaflux allows customers to use any mix of storage technologies to best meet their requirements, whether on-premises, in a public or private cloud, or a hybrid of both. Users have a global view of all the data, no matter what storage it is on, with identity and policy-based access controls.

    Laurie Coppola Mitchell, SVP of global alliances and partner marketing at Wasabi Technologies, said: “Wasabi offers high-performance, secure cloud storage at a fraction of the cost of other providers. By integrating cloud storage with Mediaflux, organizations can optimize costs, enhance security, and maintain control over their IT infrastructure.”

    As a use case example, the two companies have illustrated how their partnership can work in higher education and research institutions. More details here.

    Laurie Coppola Mitchell, Wasabi
    Laurie Coppola Mitchell

    Pricing for the joint Mediaflux and Wasabi offering is predictable, transparent, and straightforward, with Mediaflux licensing decoupled from the volume of data stored so organizations can affordably scale storage needs to hundreds of petabytes without financial strain. Wasabi customers pay one “low rate” for capacity, with no hidden fees or egress charges. The offering is available now and can be purchased through Arcitecta’s Mediaflux channels. Wasabi cloud storage can be purchased online at a per TB rate per month.

    At last month’s IT Press Tour of Boston and Massachusetts, Wasabi said it had reached 100,000 customers after continuing sales growth.

    Comment

    We covered Arcitecta and Wasabi’s partnership in a brief note yesterday. It is part of a fundamental change in the unstructured data management market that deserves more attention.

    Arcitecta and Hammerspace both provide file-based distributed, multi-vendor, multi-storage-media, and on-prem/public cloud location file and object management and orchestration, providing local-speed access to centrally managed files and objects in a global namespace. They partially overlap with Komprise, coming from a file lifecycle management background, and also Datadobi with its data migration background. In addition, there is a partial functional overlap with cloud file services suppliers CTERA, Nasuni, and Panzura, which each serve cloud-based file access to distributed locations.

    The growth in GenAI training and inference has made access to the entirety of an organization’s unstructured data more important, both for better trained models and for retrieval-augmented generation (RAG), which needs access to an organization’s private data. Because of this, previous file lifecycle management and migration specialists are encountering a need to become GenAI unstructured data supply generalists. GenAI is encouraging these suppliers to come out from their niches into a more open market.

    That is because these GenAI data sourcing needs drive a requirement for organizations to understand where their data is stored and to make it available to AI workloads. That is providing a rising data storage market demand for products that can provide maps of an organization’s data and move it or make it available to GenAI. It is for this reason that Arcitecta, CTERA, Datadobi, Data Dynamics, Egnyte, Hammerspace, Komprise, Nasuni, and Panzura will find themselves meeting each other in bids as customers cast about for products and services that help them map, organize, and orchestrate their distributed unstructured data for GenAI use.

    Hitachi Vantara becomes Hammerspace’s first major storage supplier partner

    By
    Chris Mellor
    -

    Hitachi Vantara and Hammerspace have made a deal to combine multi-source data orchestration facilities from Hammerspace with Hitachi Vantara’s enterprise-level file and block storage products, targeting data provision for GenAI workloads.

    Hitachi Vantara is a long-standing supplier of block, file, and object storage to large and medium enterprises. It is unifying its portfolio under a Virtual Storage Platform (VSP) One concept with recent VSP One block and object appliances as part of this initiative. The company has also introduced its IQ product with NVIDIA-specific GPU server targets, like BasePOD, integrated with Hitachi Vantara storage systems. This includes HCFS, rebranded WekaFS file system software (Weka Data Platform), a POSIX-compliant parallel file system integrated with Hitachi Vantara’s object storage.

    Integration with Hammerspace’s Global Data Platform adds another dimension to this storage portfolio.

    Kimberly King, Hitachi Vantara
    Kimberly King

    Kimberly King, SVP of strategic partners and alliances at Hitachi Vantara, said: “This partnership represents a strategic move to enhance our portfolio with advanced and intelligent capabilities to orchestrate data, addressing the growing demand for robust and scalable AI solutions.”

    Hammerspace’s Global Data Platform (GDP) presents a global namespace, in which data is placed, orchestrated, made available for use, and accessed as if it were local. It uses parallel NFS-based file system software to manage file and object data in globally distributed locations, across SSDs, disk drives, public cloud services, and tape media. Hitachi Vantara customers can now use GDP to access other suppliers’ storage in a single environment, and use it in GenAI training and inferencing applications. 

    By using Hammerspace’s advanced data orchestration capabilities, Hitachi Vantara customers will be able to unify data access, provide data for in-place AI, and consolidate data into a central data lake for AI workload access. The two companies say that Hammerspace’s “standards-based parallel file system will ensure that all your performance requirements are met for your AI workloads and will scale easily and linearly as your environment grows.”

    David Flynn, CEO and founder, Hammerspace, said: “We are thrilled to announce our strategic partnership with Hitachi Vantara. This collaboration represents a significant step forward in our mission to revolutionize data orchestration and storage solutions.” 

    David Flynn, Hammerspace
    David Flynn

    Hitachi Vantara is the first enterprise storage system supplier to ally directly with Hammerspace. It, along with NetApp and others, has existing storage array software capabilities to virtualize other suppliers’ arrays and treat their stored data as part of, in this case, the Hitachi SVOS environment. The Hammerspace deal extends this concept much further and works higher up the stack, so to speak. More information on Hitachi Vantara’s offerings in AI here.

    Bootnote

    Hammerspace is contributing its acquired RozoFS Mojette transform-based erasure coding to Linux for client-side erasure coding.

    1...282930...437Page 29 of 437

    ABOUT US

    Blocks & Files is a storage news, information and analysis site covering storage media, devices from drives through arrays to server-based storage, cloud storage, networking and protocols, data management, suppliers and standards. We publish news, views, opinions and analysis of storage industry events and developments, aiming to keep storage users, developers, consultants, marketeers, salespeople, support staff, analysts and everyone else involved in IT storage up to date with what’s going on.

    FOLLOW US

    © Situation Publishing, 2018-2024