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Finchetto and its optical packet switch: you can’t store light

The fundamental problem of an optical packet switch is that light cannot be stored. A light beam carrying a data packet cannot be stored and then sent on its way. That makes reading header information in a light-carried data packet near-impossible.

Hold this thought as we resume our look at photonics chip startup Finchetto. We exited the first part of this look at Finchetto with it building a photonic switch promising to be much faster than a digital (electronic) packet switch. 

At a simplistic level, a digital packet switch has a number of input ports and a number of output ports. Its job is to receive an input packet and send it out on the correct output port. So let’s say this is an Ethernet switch and a data packet comes in on an inbound port which terminates an Ethernet cable. It is composed of a header section and a data payload; all sequences of bits. The header contains packet destination addressing information. The switch receives the incoming packet and writes it to a buffer memory store. It then locates and extracts the header information and identifies the destination address. This is mapped to an output port. The data packet is then routed to that port and sent on its way along the port’s Ethernet cable.

The problem with the same data packet being carried along an optical cable is that when it comes in you can’t store the light signal’s data packet as light. You can store its bits in DRAM but then you are in the digital electronics domain, the slower electronics domain, and have exited the photonics world. 

Mike Pearcey.

What Finchetto’s co-founder, Mike Pearcey, realized was that, as the light beam carrying the data payload and header would have a specific wavelength, aka color, you could, at its origin point, transmit light in a different wavelength at exactly the same time, and this color could represent the packet’s destination, an optical control signal. So two packets arrive simultaneously at the optical packet switch; a data payload and a header, carried on two separate colors, say 1550nm and 1551nm. The switch is set up so that it will route the data payload to the output port identified by the 1551nm color. The wavelength is specific to the destination for a data packet.

Because light doesn’t stop, the speed increase can be significant. 

CEO Mark Rushworth said: “We’ve eliminated the electrical control signal, the rate limiter on how granular you can get your switching in the circuit switches. We’re talking tens of microseconds, reconfiguration time, others are looking at less than a microsecond reconfiguration time, but that’s not fast enough to do a hundred gig network even, which is fairly low small fry these days. By eliminating that electronic control signal that says; switch this way, switch that way; that’s taking tens of microseconds or hundreds of nanoseconds and replacing it with light controlling lights, we’ve reduced that switching time to low nanoseconds.”

A demo set up at Finchetto’s office shows the effect;

Finchetto optical packet switch concept demonstration.

The leftmost black unit is the optical packet switch input point, receiving data and header signals from an input port. These are sent in sequence and in parallel to a non-linear and frequency-mixing photonic processor. There are white data packets on the upper optical cable and green, yellow and red color  header information, synchronized with the correct data packets, on the lower cable. The two sets of signals arrive in sequence at the switch’s processor and it sends them onwards to the output port demultiplexer complex (right hand black truncated triangle shape) which routes them out through the correct port.

Rushworth said the processing part of the switch: “is actually taking those two parallel wavelengths and it is transposing the data onto the addressed wavelength. So only one wavelength comes out … on the destination wavelength, and then you have demultiplexer would send them out. Then you can physically get the data to the correct destination based on what wavelength it is on.”

This optical packet switch is agnostic to data carrying protocols. Rushworth says: “The packet remains integral as an Ethernet packet or Infiniband packet. Whatever protocol you’re using stays so that it can be understood at each end without any issues. We keep the same protocol that the system has.”

It’s also agnostic to data signalling speed. He said:”The interesting thing about the switch is, because it’s all optical, what that means is that it’s future-proof to future network speeds. So at the moment, cutting edge is 800 gigabits per second. They’re pushing on 1.6 terabyte per second. In two, three years it’ll be 3.2 TBps and so on. But because the switch is passive optics, it doesn’t matter what speed the signal comes in, because whatever the speed, we’ll pass it through.”

He said the timing synchronization is no problem because “the time domain for the data is so coarse in comparison to the time domain of the light.”

There is one additional aspect here. Rushworth says that: “In a high performance compute cluster, you might decide that you have different elephant flows and mice flows. Basically you’ll have clusters of packets that need to go to certain places. So you might not switch every packet.”

“A Cisco switch as it stands at the moment, has to, by definition, decode every packet to see where that data’s going to go, even if 20 packets in a row are going to the same place. That’s what it does. It reads the headers and so on. But in our case, what you can do is cluster those packets together and have session-based switching, as we call it.”

Finchetto has to build the photonic switch but also build an interface module which would be a custom NIC that is taking the digital data from the GPU, the CPU, and creating the packets and then spitting out the light in the particular way needed.

There is additional work to be done: ”You’ve got the switch product, but then, in order to make it work in a network, you’ve obviously got to have control plane and firmware and software at various levels. So we’re building a full solution, a full product solution to deliver that future network.”

Mark Rushworth.

Overall, Rushworth told us: “The bit that we need to demonstrate is that this, in the context of a network, can be demonstrated to be more performant than existing digital switches.”

“And there’s a few additional things, technical challenges that we need to solve. Flow control, for example. We now have an unblocked network, but what happens if 49,000 servers are all sending to one destination? How do you deal with that? Light doesn’t stop for anybody. So you don’t have that buffer, you don’t have that memory. What do you do about it? We’ve got a whole series of proprietary clever management techniques and overlay that makes it work as a network, which I can’t go into.”

Rushworth said Finchetto has discussed its concepts with customers and suppliers in players in the market: “They’ve come back and said, okay, these are the challenges we want to see you meet as a startup. So we’re building towards that. We have an active conversation with some big players. We’re very close to some of the OEMs that are building switches already. There’s an external demo upcoming, which will be answering those questions raised by  the homework that we’ve been given by the market.”

He said: “We’ve demonstrated the bits, but now we need to zoom out and demonstrate the system. That’s the focus at the moment. And in terms of when we would have something that would be considered a product, something that you could put into customer’s laboratories, we anticipate 12 to 18 months away.”

“That is to show pilots and proof of concept and really demonstrate it within the customer environment. Now a lot can happen and there’s a lot of different routes that you can take at that point, whether it’s co-development with a partner or whether it’s self-development.”

It’s very early days here. You can check out a Finchetto patent here to get a more detailed look at its ideas. The concept of a passive optical photonics switch is fascinating. The upcoming demonstration will be a vital hurdle for the company to overcome and then, if it greenlights further development, will involve semiconductor level work. 

Finchetto is already involved with this aspect of the development. Rushworth again: “We’ve partnered with a foundry in Switzerland called CSEM. In collaboration with them, we’ve been doing chip-based development work primarily on the device level. Photonics is light generation, manipulation, and detection. It’s that manipulation bit where we sit. Other people could do the transceiver creation and detection. The clever bit we do is the light controlling light and that’s in the manipulation. That’s the work that we’ve been doing in collaboration with CSEM and we’re out at the moment with a job for an integrated optics engineer to increasingly bring that in-house.”

Stay tuned. We’ll keep an eye on Finchetto’s progress and see how things develop. 

Hitachi Vantara extends VSP One Block fabric to Azure

Hitachi Vantara now supports its cloud-native block storage on Azure, just days after extending it from AWS to Google Cloud.

VSP One SDS (Software-Defined Storage) is now available in the Microsoft Azure Marketplace. Hitachi V says this update gives customers the ability to manage and protect data across Azure and on-premises systems. It has built-in provisioning, compression, and two-way asynchronous replication capabilities, and is managed by Hitachi Vantara’s VSP 360 software, with centralized automation, visibility and control.

Octavian Tanase

Chief product officer Octavian Tanase stated: “By bringing VSP One to Microsoft Azure, we’re helping customers extend the value of their existing investments while introducing new levels of resiliency, efficiency and simplicity.” 

By adding Azure to the VSP One SDS mix, Hitachi V says DevOps teams can migrate, test, and scale workloads across environments without disruption. They have more cloud environments at their disposal, which can help with business continuity. The company says VSP One is “engineered for continuous availability, with a target of 99.999 percent uptime, to help minimize downtime and reduce the need for redundant infrastructure.”

In its on-premises form, VSP One is accompanied by File and Object offerings. They have no native public cloud presence yet, although the development direction seems clear. A Hitachi Vantara eBook states: “Virtual Storage Platform (VSP) One is a single data plane across structured and unstructured data, including block, file, and object, so organizations can run all of their applications anywhere – on premises or in the public cloud.” 

Perhaps Hitachi V’s hybrid private/public cloud fabric could extend out to other clouds?

The concept of a hybrid data fabric covering the on-premises and three main public clouds was pioneered by NetApp. It is now supported by Dell, HPE, and others, with Hitachi V joining them to promote the idea of a unified and cross-private/public cloud block, file, and object storage environment.

Learn more about VSP One SDS on Azure here.

Storage news ticker – August 19

Dell is using Kioxia’s 245.76 TB LC9 QLC SSD with its PCIe Gen 5 interconnect in its PowerEdge servers. Arun Narayanan, SVP, Compute and Networking, Dell Technologies, said: “SSDs like the KIOXIA LC9 Series combined with Dell PowerEdge servers offer high-capacity, power-efficient solutions tailored for advanced AI workloads while optimizing TCO and data center footprints.” 

Research house Dell’Oro projects the worldwide Data Center Physical Infrastructure (DCPI) market to grow at a 15 percent CAGR from 2024 to 2029, reaching $63.1 billion by 2029 as AI-ready capacity accelerates through the mid-decade. This outlook reflects stronger-than-expected deployments to support accelerated computing workloads. Service providers (cloud and colocation) are set to grow at a 20 percent CAGR through 2029, while Enterprise increases to a 6 percent CAGR as enterprise leaders favor colocation partners to host AI infrastructure. Growth is broad-based, with North America leading and EMEA/China peaking around 2026 before moderating; AI sovereignty and export-policy shifts support momentum. Operators increasingly combine utility ties with on-site generation and other tactics. Overall, it expects only a modest impact on capacity expansion from power constraints. Buy its report here.

Content collaborator Egnyte announced its Agent Builder with a no-code framework. Users can create customized AI agents to automate time-consuming, content-intensive tasks. They have secure access to their company’s internal content and information from the public web. Egnyte’s existing AI agents serve as templates that allow non-technical users to leverage them as launching points for their agent creation that can be tested and shared across the organization to ensure consistent, reliable outputs. Customers can build custom AI agents or use agent templates.

Egnyte’s customers have already begun implementing AI agents to transform their workflows, tailored to the needs of their organization. Examples include creating agents that generate investment memos from extensive CIM documents and compare potential investment opportunities in Financial Services, crafting RFPs for subcontractors in Architecture, Engineering, and Construction (AEC), and, across industries, HR departments have created agents that help answer HR-related questions from employees, leveraging their internal policies and procedures.

To learn more about Egnyte’s AI Agent Builder, click here.

EnterpriseDB announced its sovereign data and AI platform, EDB Postgres AI integrates with Nvidia AI software. It says EDB PG AI, built with Nvidia accelerated computing and Nvidia AI Enterprise, including NIM microservices and NeMo Retriever, enables organizations to build AI and data platforms using PostgreSQL. This integration enhances EDB PG AI as a secure, high-performance platform for deploying generative and agentic AI with enterprise-grade governance, observability, and cost efficiency. Measurable outcomes for customers include:

  • 2-3x throughput improvement for data embedding tasks
  • 1.5-2x throughput improvement for retrieval performance
  • Bring new, sovereign AI applications into production 3x faster

Read more information here.

DigiTimes reports Micron is drawing back from the Chinese market and stopping its UFS 5.0 development. It says this is a positive move from the Phison and SIMO UFS controller market point of view.

UFS 5.0 is a flash storage standard for mobile and similar devices. It will succeed UFS 4.0 and provide almost double its 5.8 GBps bandwidth. The JEDEC standard is expected to be released in 2027.

TrendForce reports DRAM fabber Nanya and IC design house Etron Technology are forming a Hsinchu City, Taiwan-based JV to build High Bandwidth Memory (HBM). Nanya plans to launch new HBM products with its partners by the end of 2026, targeting applications such as AI PCs, smartphones, robotics, and automotive systems. Nanya is working with packaging and testing partner Formosa Advanced Technologies to establish 3D Through-Silicon Via (TSV) processes and multi-chip stacking capabilities.

Comment: SK hynix is making so much money from HBM, with Nvidia using more and more of the stuff. Micron and Samsung have piled into the HBM market as well. It’s likely AI Inferencing will spread to desktop, handheld, and embedded devices in the automotive and other areas. If such devices use GPUs, HBM will speed the inferencing workloads and specialized designs could be needed, providing scope for new entrants.

Quantum, the troubled tape system, object archive, video storage, deduping backup target and file management supplier, has expanded into China, India, and the ASEAN market via distribution. It has agreements with ChangHong IT (CHIT) in China, Rashi Peripherals Limited in India, Hibino Graphics Corporation (formerly NGC) in Taiwan, and ACA Pacific in ASEAN. The new channel model is designed to expand Quantum’s market reach and enhance the customer experience with extended local service and support coverage, faster time-to-delivery, and tailored technical support. Each distributor will lead go-to-market efforts in their territory, invest in sales and marketing growth, and collaborate closely with Quantum to offer regional customers tailored support and services.

Korea’s Alpha Economy outlet reports Samsung is going to supply 12-hi HBM3E memory to Nvidia, which will receive 30,000 to 50,000 units for use in water-cooled servers in the near future. Samsung did not comment.

Germany’s Heise IT news outlet reports Seagate has found a counterfeit disk drive factory, which it alleges fraudulently sells used drives as new ones by deleting SMART on-drive usage data. Malaysian officials and the drive maker said in a statement: “Members of the Seagate security team from Singapore and Malaysia, together with officials from the Malaysian Ministry of Domestic Trade, unearthed a first counterfeiting workshop in a cramped storage room outside Kuala Lumpur back in May. According to the company, this counterfeiting workshop was taking in thousands of US dollars every month.” Used WD and Toshiba drives were also allegedly being processed to appear new. Officials claimed: “The fraudsters sold the counterfeit drives online via Shopee and Lazada, two of the largest e-commerce platforms in Southeast Asia.”

Ed Filippine

Silk, which supplies software enabling databases and similar workloads to use extremely fast ephemeral block storage in the public cloud, has hired Ed Filippine as president to lead Silk’s go-to-market functions, customer success operations, and global expansion as the company accelerates adoption across mission-critical enterprise and AI workloads. He previously held executive leadership roles at Vertica, Carbon Black, and CloudHealth Technologies, where he helped scale both companies before they were acquired by HP and VMware.

Silk CEO Dani Golan said: “As enterprises push AI into production and face growing performance and cost challenges in the cloud, Ed’s leadership in GTM will be critical in expanding the Silk platform to a broader set of global customers.”

A study by researchers from UCL, UC Davis, and Mediterranea University of Reggio Calabria, to be presented and published as part of the USENIX Security Symposium, looked at GenAI browser assistants and privacy. The researchers analyzed ten of the most popular generative AI browser extensions, such as ChatGPT for Google, Merlin, and Microsoft Copilot. It uncovered widespread tracking, profiling, and personalization practices that pose serious privacy concerns, with the authors calling for greater transparency and user control over data collection and sharing practices. One assistant, Merlin, even captured form inputs such as online banking details or health data.

The paper by Yash Vekaria et al. “Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assistants”’” is published in the proceedings of the 34th USENIX Security Symposium. Read the abstract here.

Dell plugs Elasticsearch and Nvidia Blackwell GPUs into AI Data Platform

To accelerate customer AI pipelines and workloads, Dell has integrated its AI Data Platform with Elasticsearch. At the same time, the PowerEdge R7725 server runs the Nvidia RTX Pro 6000 Blackwell GPU and incorporates Nvidia’s AI Data Platform Reference Design.

The Dell AI Data Platform will soon have an unstructured data engine, built by Dell and Elastic, with vector search, semantic retrieval and hybrid keyword search capabilities. It will provide real-time, secure access to large-scale unstructured datasets for inferencing, analytics, and intelligent search, using GPU acceleration. The unstructured data engine works alongside other tools, like a federated SQL engine for querying scattered structured data, a processing engine for handling large-scale data transformation, and storage designed for fast, AI-ready access.

Ken Exner

Ken Exner, chief product officer at Elastic, stated: “With Elasticsearch vector database at the heart of the Dell AI Data Platform’s unstructured data engine, Elastic will bring vector search and hybrid retrieval to a turnkey architecture, enabling natural language search, real-time inferencing, and intelligent asset discovery across massive datasets. Dell’s deep presence in the enterprise makes them a natural partner as we work to help customers deploy AI that’s performant, precise, and production-ready.”

GPU-powered search can use the R7725 server, which Dell says is “ideal for accelerated enterprise workloads, from  visual computing, data analytics and virtual workstations.” Typically for this line of work, the servers will be fitted with SSDs – 1.6 TB to 30.72 TB NVMe as well as a range of SATA ones. SAS disk drives are also available. The servers can be linked to Dell’s external PowerScale (scale-out NAS) and ObjectScale (S3-compatible object storage) and their high-capacity stores of structured, semi-structured, and unstructured data.

As sister pub El Reg said, the RTX Pro 6000 is a workstation GPU, puts out 3,753 teraFLOPS of sparse FP4 compute, and is equipped with 96 GB of GDDR7 with 1.6 TBps of memory bandwidth. Nvidia’s AI Data Platform reference design integrates storage with its hardware and software to enable AI agents to deliver real-time responses. The Nvidia components are Blackwell GPUs, BlueField-3 DPUs, Spectrum-X networking, and Nvidia AI Enterprise software. The software bit includes NeMo Retriever and NIM microservices, and the AI-Q Blueprint.

The R7725 and also Dell’s R770 servers can run Nvidia AI reasoning models such as the latest Nemotron models for agentic AI and Cosmos world foundation models for physical AI. 

All this enables Dell to claim that this update to its AI Data Platform will “enhance unstructured data ingestion, transformation, retrieval and compute performance to streamline AI development and deployment – turning massive datasets into reliable, high quality real-time intelligence for generative AI.” 

This pair of updates “will help support enterprises on their AI journey from data ingestion to inferencing, streamlining data preparation, unifying data access across silos and delivering end-to-end enterprise-grade performance.”

The unstructured data engine in Dell AI Data Platform will be available later this year. Dell PowerEdge R7725 and R770 servers with Nvidia RTX Pro 6000 GPUs will be globally available later this year.

Read more in a Dell blog.

Databricks funding blitzkrieg  with <$100 billion valuation

Databricks cloud-based lakehouse business is growing so fast that a fresh funding round at a $100 billion-plus valuation is being closed, and the CEO dreams of it becoming a trillion dollar business.

A company statement talked of strong momentum for the AI-infused lakehouse supplier. It has more than 15,000 global customers using its Data Intelligence Platform to build AI models and agents so as to analyze their data better and faster. The amount of raised capital has not been disclosed.

Ali Ghodsi.

Co-founder and CEO Ali Ghodsi stated: “Databricks is benefiting from an unprecedented global demand for AI apps and agents, turning companies’ data into goldmines. We’re thrilled this round is already over-subscribed and to partner with strategic, long-term investors who share our vision for the future of AI.”

He prefaced this by saying: “We’re seeing tremendous investor interest because of the momentum behind our AI products, which power the world’s largest businesses and AI services. Every company can securely turn its enterprise data into AI apps and agents to grow revenue faster, operate more efficiently, and make smarter decisions with less risk.”

It says it will use the new capital to accelerate its AI strategy by expanding Agent Bricks, investing in Lakebase, its new database offering, and funding global growth. It recently added a Lakebase Postgres database layer to its lakehouse, so that AI apps and agents can run analytics on operational data there. Agent Bricks is a toolset to automate AI agent development.

Earlier this year, in March, it partnered with Anthropic, and its Claude LLM, and Palantir for LLM tuning.

Twelve year-old Databricks’ most recent funding round was a J-round at the end of 2024 raising $10 billion and taking total funding past $14 billion. Its valuation at that time was $62 billion. Since then, it added another $5 billion in debt financing and its valuation has risen 60 percent.

The annual run rate for its SQL data warehousing product was $600 million at the end of 2024, more than 150 percent higher than a year ago. It had recruited 10,000+ customers, including Block, Comcast, Condé Nast, Rivian, Shell, including more than 60 percent of the Fortune 500. Now it has up to 5,000 additional customers and we understand its annual recurring revenue was $3.7 billion in July.

Rival Snowflake has a market capitalization of $66.14 billion. Databricks investors will be hoping for an IPO payday well past that.

Disk ain’t dead yet, says Ceph shop OSNexus

Interview: VAST and Solidigm are too quick to write off Ceph disk-based storage. That’s the view of Steve Umbehocker, founder and CEO of QuantaStor Ceph storage system supplier OSNexus. He read the Solidigm/VAST Data SSD vs HDD article and wanted to better explain how Seagate+Ceph is deployed and the associated refresh cycle.

B&F: Why is that?

Steve Umbehocker

Steve Umbehocker: First, on large Ceph clusters, we enjoy the use of the Seagate Corvaults, which provide an additional layer of erasure coding beyond that with just Ceph alone to deliver 14 nines of durability based on Markov chain analysis done by Seagate. I would like to see where the equivalent VAST solution stands as a point of comparison.

The use of Corvault also gives us the ability to turn the 30 TB HDDs into 120 TB logical volumes/LUNs. The value there is that it reduces our storage cluster compute requirements by 4:1, which also reduces the power requirements for the compute aspect of the cluster by 4:1. Those compute requirements don’t seem to be mentioned anywhere in the article.

Next, on deployments, 10 PB+ would carry a seven-year extended hardware warranty and the refresh would start at year five to add another five years with another seven-year warranty on the renewal. So for a ten-year span there would be one refresh, not two. Part of the reason this is possible is due to the ADR (Seagate Autonomous Drive Regeneration) technology and the built-in auto healing with ADAPT (Autonomic Distributed Allocation Protection Technology).

B&F: Are VAST and Solidigm right about data reduction?

Steve Umbehocker: Regarding the deduplication and compression numbers, that’s not going to help for pre-compressed formats like video and image formats which are arguably the most common content type for these large and hyperscale clusters, whether it’s medical images or images from telescopes and satellites and pre-encrypted data.

It also ignores that Ceph comes with compression with multiple options including LZ4 and Zstandard (zstd). My view is that it is disingenuous to talk about compression rates that high when it doesn’t match the use case. I’m sure they are getting high compression rates for use cases like containers and VMs.

B&F: How about power efficiency?

Steve Umbehocker: With regard to power consumption and rack space usage, they do make great points. Although HDDs can use less power (7 W) versus some NVMe media (14 W), the 4x density per device between 30 TB Mosaic and 122 TB Solidigm goes to show that Seagate needs to act fast in rolling out 50 TB+ HDDs to keep up with the power efficiency that comes with these amazing densities flash vendors are hitting.

B&F: What is the effect of all this on datacenter costs?

Steve Umbehocker: I estimate about 10 KW/rack for 50 racks per EB. That’s assuming 7 W per drive over 1,060 drives plus an additional 3 KW per rack for servers. At $0.15 per KWh, that’s $1,500/month. Add in another $1,000/rack for the space and that’s $2,500 x 50 racks x 12 months: $1.5 million/year and $15 million over ten years. I think VAST estimated that to be $44 million? I don’t think my numbers are that far off as I’ve shopped for cabinet space recently and anyone deploying 50 racks is going to take into account location, utility costs, and will get discounts at scale.

So, by my estimation, [the numbers are] off by about $13 million on the hardware and as much as $29 million on the power and rack costs, which is $42 million. So $85 million minus $42 million equals $43 million. So one might argue: “OK, at $42 million, you’re still $7 million more expensive than the $35 million for the VAST storage.”

***

Umbehocker concluded that he did not believe a customer would “get 50 percent compression on video/image formats,” claiming the tally then stood at $42 million versus $70 million (2 x 35) for VAST, meaning a $28 million difference. He added: “That’s all before we start talking about software costs, networking, and compute costs of an all-flash vs a hybrid cluster. Hello elephants in the room.”

Finchetto sees the light, develops optical switch

When an optical cable terminates at a switch, its photons are transformed into electrons and digital circuitry receives and processes the light signals. How much faster things would be if you stayed with photons, like at the speed of light. In fact, you could develop an optical packet switch faster than an electrical packet switch and using less electricity.

Michael Pearcey.

That was the realization of Michael Pearcey, the CTO of UK startup Finchetto, when he was locked down at home during the Covid pandemic. He had developed an algorithm to run a processor’s ALU (Arithmetic Logic Unit) optically and reckoned it could be used to help develop optical processing functions. He and his co-founder at Finchetto, CEO Mark Rushworth, thought the concept could fly, and set about developing it with a view to productization.

We’ll examine this in a pair of articles, with this first part looking at the basics, and the second part getting into the details.

Rushworth gave me an introduction to Finchetto and the session started with me asking one of those silly questions: “Do electrons travel through a cable at the speed of light?”

The answer is no, they don’t travel along the cable at all, but an electromagnetic wave front does, propagating though the solid material of the conducting cable. It travels along the cable, which acts as a waveguide, at 50 to 99 percent of light speed in a vacuum, depending upon the cable material. Light speed is ~186,282 miles per second in a vacuum. A twisted pair copper Ethernet cable exhibits an electrical signal speed of 65 – 75 percent of light speed; ~130,400 miles per second at 70 percent. The signal will then take 0.52 microseconds to pass along 100 meters of such a cable. Light would take about 0.33 microseconds to cover a 100 meter distance in a vacuum and about 0.48 microseconds to travel along a 100 meter optical cable.

However, the Ethernet cable data rate is different and varies with encoding, bandwidth and other factors, and can be 1 Gbps (1GbE), 10Gb and so on.

Anyway, a light-carried data signal loses about 25 to 35 percent of its speed when it crosses the photon to electron boundary.

There’s another difference between light and electricity. A completed circuit is needed for electricity to flow around it. Light does not need a circuit. It can work in a vacuum or travel along a carrier that’s transparent, like an optical cable, with Rushworth saying: “It’s energy that’s propagating into an area.” In that sense, an electrical cable is an active carrier of a signal while an optical cable is passive, with Rushworth summing things up: ”What that means is that, in order for an electrical circuit to work, you have to complete it. In order for a photonic circuit to work, all you’re doing is directing it.” Directing, guiding, the light.

Mark Rushworth.

He said: ”What that means from a data comms perspective is that you can communicate data far, far faster in light than you can in electricity. And that is why you’ve got fiber optic cables and the telecommunications networks because they wanted that speed increase.”

A third basic point is that it requires a certain level of energy to get an electromagnetic wave propagating along an Ethernet cable. It takes much less energy to send a light signal the same distance. Photonics can save energy and improve data transmission performance compared to electricity. Rushworth defines photonics as: “the engineering field of light generation, light manipulation and light detection.” We can envisage lasers generating it, waveguides directing it, and photo detectors identifying it.

Photonics is particularly useful for getting the light signals closer in to the electronics involved in computing. An optical cable will currently plug into a transceiver on a network interface card (NIC). The transceiver is responsible for converting the data from the optical domain to the electronic domain and vice versa.

Finchetto is going much further than this. Rushworth said: “What we’re doing is, we’re actually building an optical switch. So more than an interconnect; we’re creating a network.”

At the moment, such switches, like Cisco ones, are electrical and can act as a bridge, an electrical domain island, between incoming and outgoing sets of optical cables. Finchetto wants to make an optical switch, a fully-optical, passive network switch, with no need for time-consuming and energy-using conversion to/from the light domain to the electrical domain.

The switch will use an integrated photonic chip. Such a chip will have wave guides etched on it, which are the rough equivalent, at an extremely small scale, of an optical cable.

Rushworth said: ”We are eliminating the electronics from the switch. So you don’t get that conversion. And that then comes back to the power and performance. You reduce the power consumption of the overall network and you improve the performance. So the latency of the data through the network [gets reduced].”

The Finchetto switch will have a port-to-port latency of 40 nanoseconds, up to 50x lower than fast electrical switches. It will draw only 38 watts, representing a 26x to 53x reduction in power compared to electrical switches.

We’ll dive deeper in how it does this, and where it could take us in part 2 of this look at Finchetto and its technology.

GigaOm’s radar reports a go go

GigaOm has released a second edition of its Globally Distributed File Systems Radar report combining players from the cloud file services, data orchestration and fabric-based scale-out filer markets, with VAST Data leading CTERA and NetApp

Chester Conforte.

The report, by analyst Chester Conforte, includes and rates eight suppliers: CTERA, Hammerspace, LucidLink, Nasuni, NetApp, Panzura, Qumulo and VAST Data. CTERA, LucidLink, Nasuni, and Panzura sell into the cloud file services market offering real-time file sharing to organizations with distributed offices and users. Hammerspace is the sole data orchestration supplier, now having a strong focus on enabling distributed data to be fed fast to GPUs for AI workloads. NetApp and Qumulo bot have on-premises-public cloud data fabrics for their filers; scale-up and clustered in NetApp’s case, and scale-out for Qumulo. VAST Data provides a parallel scale-out file and data access system using standard NFS instead of a parallel file system and has a total focus on AI with its AI OS software stack.

Our point is that we don’t see how a single purchaser looking for either a cloud file services, AI-focussed data orchestration, public-private hybrid filer or a comprehensive AI-oriented, multi-protocol AI development, operation and storage system would group all these suppliers together.

Still, GigaOm’s analyst has done that, and rated the suppliers in terms of their market positioning and and value to buyers using three variably-weighted measurement dimensions, classifying them as entrants, challengers and leaders, and plotting their position on a quasi-radar screen. (We explained how this was done here.)

We show the latest distributed file systems radar diagram below with last year’s Radar on its left for comparison;

We immediately see ObjectiveFS has been ejected from the group of rated suppliers, and both Qumulo and VAST Data have entered the group. As before, LucidLink, which sells only to MSPs, is classed as a lowly Entrant. Nasuni is the single Challenger, but has moved significantly closer to the Leaders’ area. Previous Challenger Panzura is now a Leader along with the other suppliers.

Three suppliers are classed as innovative feature players: Hammerspace, Qumulo and Lucid Link. VAST Data is the sole occupant of the innovative platform player’s quadrant with Nasuni matching its status in the feature play/maturity players’ quadrant. CTERA, NetApp amd Panzura are grouped together in the platform play/maturity area.

In terms of closeness to the center, meaning having the highest overall business value, VAST Data is numero uno, CTERA and NetApp pretty much being joint second and Qumulo fourth, followed by Hammerspace and then Panzura.

The entire GigaOm report can be read via a CTERA link.

Frost & Sullivan award

CTERA has also picked up a Frost & Sullivan award as its 2025 Global Company of the Year in the Hybrid Cloud Storage market  for the strength of its global file system platform, its security capabilities, and its commitment to launching innovative product enhancements and creating a growth-oriented business.

Karyn Price, Industry Principal, ICT at Frost & Sullivan, said: “CTERA’s platform optimizes file storage and transfer in complex, multi-cloud environments. CTERA Direct, a service within the platform, enhances file transfer speeds via its ultra-fast,  edge-to-cloud file transfer protocol, which provides fast movement of data for data-heavy workloads,” said Karyn Price, Industry Principal, ICT, Frost & Sullivan. “Perhaps most compelling is the military-grade security capabilities that CTERA offers to its  customers. The CTERA platform provides native data protection and restoration capabilities, enabling customers to eliminate separate data protection services if they choose.”

That must be music to the ears of CTERA’s workforce. 

There is an accompanying diagram, another quasi radar screen, with a different layout and organization from that used by GigaOm. Read Frost & Sullivan interpretation notes here. The further away from the center the better a company is doing. This is completely the opposite of the rating depiction in GigaOm’s Radar where the closer you are to the center the better. Different analyst, different rules.

Frost & Sullivan’s radar screen shows that CTERA is in a group along with Cloudian, Peer, LucidLink, NetApp, Hammerspace, Panzura and Nasuni. But not VAST Data nor Qumulo. Different analyst, etc.

Nasuni is the overall leader, followed by CTERA and then, closely, by Panzura.

The Frost & Sullivan CTERA award document can be viewed here.

Sandisk gets back to growth

Sandisk recovered from its poor previous quarter with a 12 percent sequential revenue rise as NAND undersupply enabled it to raise prices.

Revenues in the final fiscal 2025 quarter, ended June 27, were $1.9 billion, a 7.9 percent year-on-year increase and beating its $1.85 billion guidance, with a GAAP loss of $23 million; quite a switch around from the year-ago $120 million profit. A transition to a new NAND node, BiCS8, has caused high start-up costs in the joint-venture fabs operated with Kioxia. Full FY fy2025 revenues were $7.36 billion, 10 percent higher than the prior year, with a GAAP loss of $1.6 billion, almost double the year-ago $96 million loss.

David Goeckeler.

CEO David Goeckeler stated: “Sandisk delivered strong results this quarter, with revenue and non-GAAP EPS exceeding our guidance.” Looking ahead he said: ”With High Bandwidth Flash (HBF), we are creating a new paradigm for AI inference solutions. With demand improving and industry fundamentals strengthening, we are well-positioned to drive sustainable growth, expand margins, and generate strong cash flow.”

HBF is the use of high-bandwidth memory type die stacking and multi-channel interposer links to an on-package GPU to provide a high-speed NAND cache to offload the GPU’s memory. Goeckeler noted: “This is a technology that can play from the edge, so PCs, smartphones, all the way into the cloud. …We think it’s a new paradigm for how [AI] inference is driven.”

He is envisaging a smartphone’s processor having both DRAM and HBF attached to it. 

Webush analyst Matt Bryson commented: “With consensus anticipating an even steeper improvement (going back to Sandisk’s initial comments during its analyst day before tariffs and tariff uncertainty took a bite out of the expected market), numbers ended up disappointing vs. expectations with the stock falling ~10 percent.”

Let’s look at its quarterly revenue history;

When it was part of Western Digital – Q1 fy2021 to Q2 fy2024 in our chart – the Sandisk operation’s profits were not revealed.

The revenue numbers from FY 2021 onwards, when Sandisk was part of Western Digital, shows a peak in the second fiscal 2022 quarter followed by a slump to the third FY 2023 quarter and then a gradual climb until growth flattened in Q1 to Q2 FY 2025 and went negative in the third quarter after the spin-off from Western Digital. That dip has been reversed; Goeckeler commented that “overall demand exceeded supply” in the earnings call. This enabled prices to rise.

CFO Louis Visoso said “bit shipments and average selling prices were up mid single digits” sequentially. The revenue growth was helped by price increases, particularly in the client area, with more increases coming, and the guidance beat was due to “better than expected bits growth.” 

The Sandisk business has been loss-making for three years and Goeckeler says he wants to change that.

The company has laid off approximately 200 employees, which follows the revenue dip in the previous quarter.

Financial summary

  • Gross margin: 36.4 percent vs year-ago 26.4 percent
  • Operating cash flow: $94 million vs year-ago $94 million
  • Free cash flow: $77 million vs year-ago -$130 million
  • Cash & cash equivalents: $1.5 billion vs $328 million a year ago
  • Diluted EPS: $0.29

Sandisk, which became independent from Western Digital in February, makes NAND chips in a fab joint-venture with Kioxia, and builds them into SSDs which it sells into three business segments: Cloud (hyperscalers and enterprise data centers), client (PCs, notebooks, mobile, automotive), and consumer (retail), representing 11.2, 58, and 30.8 percent of overall revenues respectively. It’s an unbalanced business from that point of view. The segment revenues were:

  • Cloud: $213 million, up around 18 percent Y/Y and 8 percent Q/Q
  • Client: $1.1 billion, flat Y/Y, up 19 percent Q/Q
  • Consumer: $585 million, up about 17 percent Y/Y, 2 percent Q/Q 
Low, medium and high revenue tiers are evident here.

It’s using its main BiCS6 (162-layer) NAND for data center and client drives, transitioning to newer BiCS8 (218-layer) with startup costs, and making consumer drives with older BiCS5 112-layer product. The layer count, or node, transitions brings down the cost/GB and enables Sandisk to earn more revenue from selling the same amount of capacity. The transitions take time, with Goeckeler saying that, in: “Q4, …about 7 percent of our bids were BiCS8, and we will be somewhere between 40 – 50 percent by the end of fiscal year 2026.”

Sandisk says it has “strategic positioning for growth in data center in compute and storage solutions” where the surge in demand for AI processing is driving demand for high-capacity and high-performance SSD storage. The company saw “Clear momentum with customers in AI-driven workloads and hyperscale demand” with “explosive growth in AI and cloud infrastructure build-outs.“ It has announced a 256 TB UltraQLC drive and is “advancing customer qualifications for compute and storage eSSDs, including with NVIDIA GB300 and multiple hyperscalers.”

There is scope here for it to grow its cloud revenues substantially. Goeckeler commented: “data center represented over 12 percent of our total bits shipped, a meaningful milestone as we scale in this critical part of the market.” Progress will not be that quick, with Goeckeler saying: “Expansion into the data center space requires sustained effort given its long qualification cycles.”

The plan: “is to qualify our high capacity Ultra QLC platform at several major Tier one customers by the end of fiscal year 2026. On the compute enterprise SSD front, we are encouraged by the progress we have made in qualifying solutions with key customers, including an ongoing qualification with the second major hyperscaler and other customers using the Nvidia GB300.”

Next quarter’s guidance envisages revenues of $2.15 billion +/- $50 million, a 14 percent uplift at the mid-point on the year-ago quarter’s $1.9 billion. Visoso said: “We expect revenue growth to come from bits growth and higher average selling prices with similar contributions from both drivers.”

Goeckeler’s view is that: “As we enter fiscal year ‘twenty six, we see momentum in our product portfolio, an improving supply and demand environment and early benefits from our pricing actions.” Sandisk is seeing very low double-digits bit demand growth with supply under that, keeping prices higher than otherwise. It sees the under-supplied market continuing through 2026.

He noted that the tariff situation is evolving dynamically: “It’s something you got to stay very close to on a day to day basis and tariffs are another thing that are part of that equation. We stay very close to that.” Overall: “We’re very confident in our ability to navigate this whole situation with our global footprint.”

HBF Bootnote

Goeckeler said: “We’ve got a controller to design and all the interfaces to standardize around that. And doing it with an industry partner [SK hynix], we think, is the right way to go to move this along drive adoption as quickly as possible. …we think the industry is going to need this kind of capacity in the memory architecture to drive inference at scale.”

On timing: “Next year we’ll be sampling the die for the NAND and then early 2027 we’ll have the controller that goes along with that.”

HBM has driven SK hynix revenues extraordinarly high. If HBF becames a standard fit for AI-capable processors in a whole range of devices, from hand-held through embedded to desktop and servers, then it could potentially drive Sandisk revenues to similar heights.

Shhh. Lenovo’s silent Infinidat acquisition

A hand holding a chip against a swirling background. The chip has the letters 'AI' on it.

If you look for any mention of storage or the ongoing Infinidat acquisition in Lenovo’s latest and record results you will be disappointed. The S word is just not mentioned.

Revenues for the Beijing-headquartered, Chinese-owned Lenovo in its first fiscal 2026 quarter, ended June 30, were up 22 percent Y/Y to $18.3 billion, with a profit of $505 million, up 108 percent. That’s profit measured by Hong Kong Financial Reporting Standards (HKFRS). Lenovo prefers its non-HKFRS measure of $389 million, up 22 percent Y/Y as the HKFRS measure was affected by a non-cash fair value gain on warrants, resulting from share price movements. Lenovo refers investors to focus more on its actual operating performance and non-Hong Kong FRS measures.

Chairman and CEO Yuanqing Yang stated: “By leveraging the resilience and flexibility of our supply chain and operational excellence, we overcame challenges brought by tariff volatility and the geopolitical landscape and achieved significant growth in both top and bottom lines. These record Q1 results underscore our ability to deliver on our promise to preserve competitiveness and continuously grow our business.”

The three component businesses all grew at double digits;

  • SSG (services): $2.3 billion, a rise of 20 percent
  • ISG (servers, storage, etc for business): $4.3 billion with a 36 percent increase
  • IDG (PCs): $13.46 billion, climbing 18.4 percent.

This is great with, Lenovo saying, “The PC business reporting particularly strong numbers following the highest year-on-year revenue growth rate in 15 consecutive quarters and an all-time high market share of 24.6 percent.” 

CFO Winston (Shao-Min) Cheng said in the earnings call: “ISG continued to broaden its customer base across both the CSP and ESMB segments in the first quarter with wins in cloud computing, security, content delivery, high-performance computing and AI server offerings applied across a range of leading educational institutions, financial companies and AI infrastructure providers.”

A standout item in ISG was the AI infrastructure business where “revenue more than doubled year-on-year with a robust pipeline and a clear product roadmap ahead. Revenue from industry-leading liquid cooling [Neptune] solutions grew 30 percent year-on-year.” AI infrastructure revenue more than doubled Y/Y and Lenovo has a “strong pipeline.” There was, it said: “China business revenue hypergrowth Y/Y.”

ISG sells into the Cloud Service Provider (CSP) and what it terms the ESMB (Enterprise, Small and Medium Business) market sectors. In other words the public cloud people on the one hand and on-prem organizations on the other. Revenues are split roughly 50:50 between them. Most if not all storage will go to ESMB customers. And most of that is OEM’d NetApp ONTAP storage sold as ThinkSystem DG (QLC flash), DM (flash and hybrid unified), and DE (flash and hybrid SAN). 

Now it is buying Infinidat with completion later this year, and its products are clearly enterprise, and high-end enterprise at that.

Cheng said in the earnings call: “ISG recorded an operating loss of $86 million in the first fiscal quarter. Profitability was temporarily affected by strategic investments to enhance our long-term AI capabilities and accelerate the transformation of our ESMB business.” There was no explanation of what these ”strategic investments” were, and no analysts on the earnings call asked about them. In our experience, US analysts would have been all over the Lenovo execs about this.

The only strategic investment we know about is buying Infinidat and that $86 million operating loss could reflect the cost of that acquisition. We can see Infinidat helping to transform its ESMB business and, with Infinidat emphasizing its RAG capabilities, it will help with AI too. To support that idea we note that Infinidat has been awarded a “Best of Show” award for the “Most Innovative Artificial Intelligence (AI) Application” at the 2025 FMS: The Future of Memory and Storage conference. 

CMO Eric Herzog said: “Our industry acclaimed InfiniBox G4 platform, coupled with our AI RAG reference architecture, delivers an enterprise storage capability that uniquely ensures the accuracy and contextual relevance of AI models to answer queries autonomously. Winning the prestigious Best of Show award at this year’s FMS conference is a testament to Infinidat’s strategic role in AI-centric enterprise environments.”

Lenovo reckons that the AI era brings huge opportunities for devices, infrastructure, solutions and services. Yang said: “Looking ahead, ISG is committed to investing in driving long-term growth and value through strategic market expansion, E/SMB business model transformation, AI infrastructure innovation and product development, to stay ahead of the AI curve and provide differentiated global competitiveness.”  

VDURA’s view: SSD or HDD? It’s not either/or; it’s both

An NVMe-SSD + HDD architecture on a true parallel file system wins on $/TB, watts/TB, and long-term TCO over SSD-only or HDD-only architectures—without  giving up GPU performance.

So says HPC/AI storage supplier VDURA’s Chris Girard, VP of Product Management. He has taken a look at the Solidigm/VAST SSD-vs HDD TCO claims and the Pure Storage SSD superiority over disk in power-efficiency and carbon emissions study, and says they miss the point: “$/TB reality still favors capacity HDD, by a wide margin. 30 TB enterprise HDDs are now broadly available and are ~$20/TB. Ultra-high-capacity enterprise SSDs (61.44TB–122.88TB QLC) list for thousands of dollars per drive. Recent retail/contract pricing is ~$90-$112/TB.” 

He provides a table to illustrate this; 

We charted the Cost $/TB column to bring out the differences;

Girard declares: “That pricing gulf (4.5 – 6x cost difference $/TB) is why Hyperscalers and enterprise customers continue to place the bulk of warm/cold data on HDD, and why VDURA customers buy flash for performance, disk  for capacity, in one system.”

Chris Girard.

He moves on to the 10-year TCO data from Solidigm/VAST and says: “The Solidigm/VAST model assumes two full HDD fleet replacements in 10 years and credits VAST software with dramatic “effective capacity” via dedupe + compression to shrink the SSD count. As your article notes, if you apply a more realistic six-year average life for HDDs (aligned with recent Backblaze updates) the claimed advantage narrows materially. (VDURA sees some customers in hybrid environments operating for over 10 years)”

Girard points out: “More importantly, data-reduction ratios are workload-dependent. … Designing a TCO case around fixed 2.5:1 savings risks misrepresenting mixed AI/HPC data. VDURA’s software suite includes equivalent data reduction technologies as an umbrella capability, optimized for both all-flash and hybrid environments. We’ve seen real-world ratios exceeding 2:1 in enterprise workloads,  allowing us to achieve similar efficiencies.”

On that basis: “Without these, the [Solidigm/VAST] study admits SSD TCO would balloon to $66.13 million. But with VDURA’s tools normalizing the data reduction factor and a 10-year lifecycle without refreshes, our flexible hybrid systems not only match that efficiency but leverage HDDs’ inherent cost-per-TB edge to deliver substantially lower upfront acquisition costs and overall TCO compared to all-flash systems. 

Turning to power-efficiency he provides electricity use numbers;

  • 61.44 TB enterprise SSDs (e.g., Solidigm D5-P5336) draw ~25 W active and ~5 W idle — equal to roughly 0.33–0.41 W/TB active and ~0.08 W/TB idle. 
  • 30 TB HDDs draw ~6.9 W average active and ~9.5 W max, or about 0.23–0.32 W/TB at today’s densities. 

Girard comments: “For hot, high-IOPS datasets, flash’s active efficiency and latency advantages are unmatched. But for warm or cold data sitting in capacity tiers, HDDs can match or even beat large QLC SSDs on watts per terabyte,  especially when systems are optimized for capacity per rack rather than peak IOPS. Hybrid architectures exploit this balance, delivering flash where performance is critical and HDD where cost-per-TB and energy efficiency matter most.”

Carbon emissions are next in Girard’s sights: “Pure Storage’s carbon/power claims rely on selective comparisons. [It] uses device-level 10 MB/sec/TB benchmarks that ignore system-level throughput and hybrid tiering.  [It] shortchanges HDDs (and even SSDs) with 5-year lifespans while granting DFMs (Direct Flash Module) 10 years, skewing lifecycle emissions math.”

Also Pure: “Compares “modest” 2RU x 24-drive HDD enclosures to denser 5RU DFM configs, ignoring that real-world 4U HDD enclosures pack 100+ drives, boosting density 68 percent and slashing rack space, power,  and CO₂e by up to 73 percent versus all-flash.” 

In Girard’s view: “VDURA delivers one parallel file system with an NVMe flash first design dynamically balancing hot data on SSDs and cold data on high-capacity HDDs. We minimize wear on spinning disks and eliminate the need for multiple  refresh cycles. This approach drives a significantly lower 10-year TCO for 1 EB than the study’s HDD baseline, and beats all-flash at scale, by fine-tuning the SSD/HDD mix for the lowest overall cost without massive upfront capital.”

VDURA has a 10-year drive assurance scheme for both flash and disk drives. Its “hybrid design has delivered up to 60 percent lower 10-year TCO versus all-flash proposals  that sized on optimistic reduction ratios and premium QLC pricing. Power, space, and media refresh are where the savings accrue.”

But VDURA also has, Girard asserts, a performance advantage as well: “VDURA solutions outperform VAST and Pure on both a per-node basis and an aggregate cluster basis, delivering superior write and read throughput, IOPS, and metadata scalability—thanks  to being a true parallel file system that is easy to deploy, use, and manage even at exabyte scale.”

He concludes: “Enterprises don’t need to choose between cost, capability, and environmental  impact; our tailored solutions provide all three.” Or performance either.

VAST Data AI OS inside South Korea sovereign AI cloud GPU service  

VAST Data is providing its AI OS, including all-flash storage, to the SK Telecom Petasus AI Cloud which provides GPU-as-a-Service (GPUaaS) in South Korea’s sovereign AI cloud.

SK Telecom, South Korea’s main telecommunications supplier, is evolving into an AI company. It is building an AI infrastructure based on a Haein cluster of Supermicro GPU servers with Nvidia Blackwell GPUs, networking and storage. A Petasus GPUaaS AI cloud will use this cluster. It virtualizes the GPU, networking, and storage cluster, and provides it to multiple tenants. 

VAST Data is contributing to the storage part of the Petasus AI Cloud. 

DK Lee.

DK Lee, VP and Head of SK Telecom’s AI DC Lab, stated: “VAST Data’s unified architecture has been instrumental in helping us move from legacy bare-metal deployments to a fully virtualized, production-grade AI cloud.” He said the VAST AI OS provides performance, simplicity, flexibility, and fast scalability.

“With VAST, we’re enabling a GPUaaS platform that meets the exacting needs of government, research, and enterprise AI customers in South Korea.”

Sunil Chavan, VP APAC at VAST Data added his view: “From our earliest conversations, it was clear that SKT needed cutting-edge infrastructure to match the speed and complexity of enterprise-grade uptime and nation-state inference and training. By eliminating traditional bottlenecks around data movement, provisioning, and security, VAST is enabling SKT to launch a sovereign and secure AI infrastructure that offers  speed and flexibility at scale for Korea.” 

The Petasus cloud will enable AI model development and deployment within South Korea’s borders. Users – tenants – need not provision their own bare metal infrastructure. They specify the GPU resources they need and a virtual GPU server system, a virtual cluster, is spun up for them in as little as 10 minutes. It automatically provisions and isolates GPU and storage resources, including their networking fabrics, to match each tenant’s specific requirements. Each virtual cluster will, it’s claimed, match bare metal performance. 

An SK Telecom diagram illustrates the cloud components and multi-tenancy;

This GPUaaS cloud includes VAST’s disaggregated, shared-everything (DASE) platform on Supermicro servers built with NVIDIA’s HGX architecture.

Tenant workloads will be Isolated with data privacy and performance guarantees. The system provides multi-protocol data access with no need for client-side gateways or proprietary protocols. SK Telecom says the system will have carrier-grade reliability.

SK Telecom’s Haein Cluster has been selected for the Ministry of Science and ICT’s “AI Computing Resource Utilization Enhancement(GPU Rental Support) Program” in Korea, and will be used for the development of national AI foundation models.

Read more in a VAST blog: SK Telecom and VAST Data: Breaking the GPU Virtualization Barrier, and also in a VAST podcast.

Comment

DDN has been deeply involved with SK Telecom – witness this Youtube video – and SK Telecom execs have presented at DDN events about the development of the SK Telecom AI Cloud, its Petasus virtualization, and DDN storage use. Whether DDN is still involved with the SK Telecom AI Cloud remains to be seen.