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170: FMS25 wrap-up with Jim Handy, Objective Analysis

Jim Handy, General Director at Objective Analysis and I were at FMS25 in Santa Clara last week and there was a lot of news going around. Jim’s been on our show just about every year to discuss FMS news, And with the recent focus beyond flash, it’s even harder for one person to keep up.

Much of the discussion at FMS was on HBM4, new QLC capacity points, UAlink/UCe for chiplets, 100M IOP SSDs, and more. Listen to the podcast to learn more.

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Th.ere was not as much on CXL as in past shows. and ditto on increasing layer counts to drive more NAND capacity. A couple of years ago layer counts were all they talked about. And CXL was the major change to hit the data center. Jim’s view (and Jason’s) was that CXL was as a way for hyperscalers to make use of DDR4 DRAM but that need has passed now.

As for layer counts they are still going up but not as fast. And the economics of 3D scaling now have to compete with 2D scaling and “virtual scaling”.

But UAlink and UCe were active topics both of which are used to tie together chiplets in CPUs to build SoCs. SSD vendors are starting to use chiplet architectures to build their massive capacity SSDs and UAlink/UCe would be a way to architect these.

SLC NAND is back to support very high performance SSDs or as a replacement for SCM (storage class memory or Optane). One vendor talked about reaching 100M (random 512B read) IOPS for a single SSD. Current SCL flash can do ~10M IOPS, next gen is speced to do ~30M and the one following would be 100M. One challenge is that current SSDs do 4Kbyte IO and it still takes a msec. or so to erase a page and reading a page isn’t that fast. But the performance is for read only activity.

HBM4 was one topic at the show but the newest wrinkle was HB Flash, or putting SSDs behind HBM to support GPU caching (SSD to HBM to GPU). This would allow more data to be quickly accessed by a GPU.

Jim also mentioned that there’s some interest in narrowing HBM access width, currently 1Kb and increasing to 2Kb with HBM4. This width, and all the pins it requires, limits how many HBM chips one can surround a GPU with. If HBM had a narrower interface more HBM chips could surround a GPU, increasing memory size and perhaps memory bandwidth. HBM4 seems to be going the wrong way but with narrower width HBM, they could easily double the number of HBM chips surrounding a GPU.

They were also showing off a 40 SSD 2U chassis using E.2 form factor SSDs. Pretty impressive and given the capacity on offer a lot of storage per RU.

Speaking of capacity one vendor announced a 246TB QLC SSD, roughly a 1/4PB in a single SSD. With 24 of these per 2U shelf, one could have a >1/10 Exabyte, (>100 PB) in a 40U rack. It looks like no end in sight for SSD capacities. And we aren’t even talking about PLC yet.

At the other end of SSD capacity, it appears that M.2 SSDs were getting hotter on one side (controller side) than the other, throttling performance. So one vender decided to provide heat (liquid cooling) pipes between the two sides to equalize thermal load.

Jim Pappas (lately of Intel) won the lifetime achievement award from FMS. Jim’s accomplishments span a wide swath of storage technology but at the award ceremony he waxed on his work on the USB connector. He said his will stipulates that once he is interned in the ground, they are to take out the casket and spin it around 180 degrees and put it back down again. 🙂

There were quite a number of side topics not directly related to FMS25 on the podcast which were interesting in their own right, but I think i’ll leave it here.

Jim Handy, General Director Objective Analysis

Jim Handy of Objective Analysis has over 35 years in the electronics industry, including 20 years as a leading semiconductor and SSD industry analyst. Early in his career he held marketing and design positions at leading semiconductor suppliers including Intel, National Semiconductor, and Infineon.

A frequent presenter at trade shows, Mr. Handy is known for his technical depth, accurate forecasts, widespread industry presence and volume of publication.

He has written hundreds of market reports, articles for trade journals, and white papers, and is frequently interviewed and quoted in the electronics trade press and other media. 

He posts blogs at www.TheMemoryGuy.com, and www.TheSSDguy.com

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169: GreyBeards talk AgenticAI with Luke Norris, CEO&Co-founder, Kamiwaza AI

Luke Norris (@COentrepreneur), CEO and Co-Founder, Kamiwaza AI, is a serial entreprenaur in Silverthorne CO, where the company is headquartered.. They presented at AIFD6 a couple of weeks back and the GreyBeards thought it would be interesting to learn more about what they were doing, especially since we are broadening the scope of the podcast, to now be GreyBeards on Systems.

Describing Kamiwaza AI is a bit of a challenge. They settled on “AI orchestration” for the enterprise but it’s much more than that. One of their key capabilities is an inference mesh which supports accessing data in locations throughout an enterprise across various data centers to do inferencing, and then gathering replies/responses together, aggregating them into one combined response. All this without violating HIPPA, GDPR or other data compliance regulations.

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Kamiwaza AI offer an opinionated AI stack, which consists of 155 components today and growing that supplies a single API to access any of their AI services. They support multi-node clusters and multiple clusters, located in different data centers, as well as the cloud. For instance, they are in the Azure marketplace and plans are to be in AWS and GCP soon.

Most software vendors provide a proof of concept, Kamiwaza offers a pathway from PoC to production. Companies pre-pay to install their solution and then can use those funds when they purchase a license.

And then there’s their (meta-)data catalogue. It resides in local databases (and replicated maybe) throughout the clusters and is used to identify meta data and location information about any data in the enterprise that’s been ingested into their system.

Data can be ingested for enterprise RAG databases and other services. As this is done, location affinity and metadata about that data is registered to the data catalogue. That way Kamiwaza knows where all of an organization’s data is located, which RAG or other database it’s been ingested into and enough about the data to understand where it might be pertinent to answer a customer or service query.

Maybe the easiest way to understand what Kamiwaza is, is to walk through a prompt. 

  • A customer issues a prompt to a Kamiwaza endpoint which triggers,
  • A search through their data catalog to identify what data can be used to answer that prompt.
  • If all the data resides in one data center, the prompt can be handed off to the GenAI model and RAG services at that data center. 
  • But if the prompt requires information from multiple data centers,
  • Separate prompts are then distributed to each data center where RAG information germane to that prompt is located
  • As each of these generate replies, their responses are sent back to an initiating/coordinating cluster
  • Then all these responses are combined into a single reply to the customer’s prompt or service query.

But the key point is that data located in each data center used to answer the prompt are NOT moved to other data centers. All prompting is done locally, at the data center where the data resides.  Only prompt replies/responses are sent to other data centers and then combined into one comprehensive answer. 

Luke mentioned a BioPharma company that had genonome sequences located in various data regimes, some under GDPR, some under APAC equivalents, others under USA HIPPA requirements. They wanted to know information about how frequent a particular gene sequence occurred. They were able to issue this as a prompt at a single location which spun up separate, distributed prompts for each data center that held appropriate information. All those replies were then transmitted back to the originating prompt location and combined/summarized.

Kamiwaza AI also has an AIaaS offering. Any paying customer is offered one (AI agentic) outcome per month per cluster license. Outcomes could effectively be any AI application they would like to perform.

One outcome he mentioned included:

  • A weather-risk researcher had tons of old weather data in a multitude of formats, over many locations, that had been recorded over time.
  • They wanted to have access to all this data so they can tell when extreme weather events had occurred in the past.
  • Kamiwaza AI assigned one of their partner AI experts to work with the researcher to have an AI agent comb through these archives, transform and clean all the old weather data into HTML data more amenable to analysis . 
  • But that was just the start.. They really wanted to understand the risk of damage due to the extreme weather events. So the AI application/system was then directed to go and gather from news and insurance archives, any information that identified the extent of the damage from those weather events. 

He said that today’s AgenticAI can implement a screen mouse click and perform any function that an application or a human could do on a screen. Agentic AI can also import an API and infer where an API call might be better to use than a screen GUI interaction.

He mentioned that Kamiwaza can be used to generate and replace a lot of what enterprises do today with Robotics Process Automation (RPAs). Luke feels that anything an enterprise was doing with RPA’s can be done better with Kamiwaza AI agents.

SaaS solution tasks are also something AgenticAI can easily displace . Luke said at one customer they went from using SAP APIs to provide information to SAP, to using APIs to extract information from SAP, to completely replacing the use of SAP for this task at the enterprise. 

How much of this is fiction or real is subject of some debate in the industry. But Kamiwaza AI is pushing the envelope on what can and can’t be done. And with their AI aaS offering, customers are making use of AI like they never thought possible before. .

Kamiwaza AI has a community edition, a free download that’s functionally restricted, and provides a desktop experience of Kamiwaza AI’s stack. Luke sees this as something a developer could use to develop to Kamiwaza APIs and test functionality before loading on the enterprise cluster. 

We asked where they were finding the most success. Luke mentioned anyone that’s heavily regulated, where data movement and access were strictly constrained. And they were focused on large, multi-data center, enterprises.

Luke mentioned that Kamiwaza AI has been doing a number of hackathons with AI Tinkerers around the world. He suggested prospects take a look at what they have done with them and perhaps join them in the next hackathon in their area.

Luke Norris, CEO & Co-Founder, Kamiwaza AI

Luke Norris is the co-founder of Kamiwaza.AI, driving enterprise AI innovation with a focus on secure, scalable GenAI deployments. With extensive experience raising over $100M in venture capital and leading global AI/ML deployments for Fortune 500 companies.

Luke is passionate about enabling enterprises to unlock the full potential of AI with unmatched flexibility and efficiency.

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168: GreyBeards Year End 2024 podcast

It’s time once again for our annual YE GBoS podcast. This year we have Howard back making a guest appearance with our usual cast of Jason and Keith in attendance. And the topic de jour seemed to be AI rolling out to the enterprise and everywhere else in the IT world. 

We led off with our discussion from last year, AI (again) but then it was all about new announcements, new capabilities and new functionality. This year it’s all about starting to take AI tools and functionality and make them available to help optimize organizational functionality.

We talked some about RAGs and Chatbots but these seemed almost old school.

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Agentic AI

Keith mentioned Agentic AI which purports to improve businesses by removing/optimizing intermediate steps in business processes. If one can improve human and business productivity by 10%, the impact on the US and world’s economies would  be staggering.

And we’re not just talking about knowledge summarization, curation, or discussion, agentic AI takes actions that would have been previously done by a human, if done at all.  

Manufacturers could use AI agents to forecast sales, allowing the business to optimize inventory positioning to better address customer needs. 

Most, if not all, businesses have elaborate procedures which require a certain amount of human hand holding. Reducing human hand holding, even a little bit, with AI agents, that never slees, and can occasionally be trained to do better, could seriously help the bottom and top lines for any organization 

We can see evidence of Agentic AI proliferating in SAAS solutions, i.e., SalesForce, SAP, Oracle and all others are spinning out Agentic AI services.

I think it was Jason that mentioned GEICO, a US insurance company, is re-factoring, re-designing and re-implementing all their applications to take advantage of Agentic AI and other AI options. 

AI’s impact on HW & SW infrastructure

The AI rollout is having dramatic impacts on both software and hardware infrastructure. For example, customers are building their own OpenStack clouds to support AI training and inferencing.

Keith mentioned that AWS just introduced S3 Tables, a fully managed services meant to store and analyze massive amounts of tabular data for analytics. Howard mentioned that AWS’s S3 Tables had to make a number of tradeoffs to use immutable S3 object storage. VAST’s Parquet database provides the service without using immutable objects.

Software impacts are immense as AI becomes embedded in more and more applications and system infrastructure. But AI’s hardware impacts may be even more serious.

Howard made mention of the power zero sum game, meaning that most data centers have a limited amount of power they support. Any power saved from other IT activities are immediately put to use to supply more power to AI training and infererencing.

Most IT racks today support equipment that consumes 10-20Kw of power. AI servers will require much more

Jason mentioned one 6u server with 8 GPUS that cost on the order of 1 Ferrari ($250K US), draws 10Kw of power, with each GPU having 2-400 GigE links not to mention the server itself having 2-400 GigE links. So a single 6U (GPU) server has 18-400GbE links or could need 7.2Tb of bandwidth.

Unclear how many of these one could put in a rack but my guess is it’s not going to be fully populated. 6 of these servers would need >42Tb of bandwidth and over 60Kw of power and that’s not counting the networking and other infrastructure required to support all that bandwidth.  

Speaking of other infrastructure, cooling is the other side of this power problem. It’s just thermodynamics, power use generates heat, that heat needs to be disposed of. And with 10Kw servers we are talking a lot of heat. Jason mentioned that at this year’s SC24 conference, the whole floor was showing off liquid cooling.  Liquid cooling was also prominent at OCP.

At the OCP summit this year Microsoft was talking about deploying near term 150Kw racks and down the line 1Mw racks. AI’s power needs are why organizations around the world are building out new data centers in out of the way places that just so happen to have power and cooling nearby. 

Organizations have an insatiable appetite for AI training data. And good (training) data is getting harder to find. Solidigm latest 122TB SSD may be coming along just when the data needs for AI are starting to take off.

SCI is pivoting

We could have gone on for hours on AI’s impact on IT infrastructure, but I had an announcement to make.

Silverton Consulting will be pivoting away from storage to a new opportunity that is based in space. I discuss this on SCI’s website but the opportunities for LEO and beyond services are just exploding these days and we want to be a part of that. 

What that means for GBoS is TBD. But we may be transitioning to something more broader than just storage. But heck we have been doing that for years.

Stay tuned, it’s going to be one hell of a ride

Jason Collier, Principal Member Of Technical Staff at AMD, Data Center and Embedded Solutions Business Group

Jason Collier (@bocanuts) is a long time friend, technical guru and innovator who has over 25 years of experience as a serial entrepreneur in technology.

He was founder and CTO of Scale Computing and has been an innovator in the field of hyperconvergence and an expert in virtualization, data storage, networking, cloud computing, data centers, and edge computing for years.

He’s on LinkedIN. He’s currently working with AMD on new technology and he has been a GreyBeards on Storage co-host since the beginning of 2022

Howard Marks, Technologist Extraordinary and Plenipotentiary at VAST Data

Howard Marks is Technologist Extraordinary and Plenipotentiary at VAST Data, where he explains engineering to customers and customer requirements to engineers.

Before joining VAST, Howard was an independent consultant, analyst, and journalist, writing three books and over 200 articles on network and storage topics since 1987 and, most significantly, a founding co-host of the Greybeards on Storage podcast.

Keith Townsend, President of The CTO Advisor, a Futurum Group Company

Keith Townsend (@CTOAdvisor) is a IT thought leader who has written articles for many industry publications, interviewed many industry heavyweights, worked with Silicon Valley startups, and engineered cloud infrastructure for large government organizations. Keith is the co-founder of The CTO Advisor, blogs at Virtualized Geek, and can be found on LinkedIN.

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167: GreyBeards talk Distributed S3 storage with Enrico Signoretti, VP Product & Partnerships, Cubbit

Long time friend, Enrico Signoretti (LinkedIn), VP Product and Partnerships, Cubbit, used to be a common participant at Storage Field Day (SFD) events and I’ve known him since we first met there. Since then, he’s worked for a startup and a prominent analyst firms. But he’s back at another startup and this one looks like it’s got legs.

Cubbit offers Distributed S3 compatible object storage that offers geo-distribution and geo-fencing for object data, in which the organization owns the hardware and Cubbit supplies the software. There’s a management component, the Coordinator, which could run on your hardware or as a SaaS service they provide but other than that, IT controls the rest of the system hardware. Listen to the podcast to learn more.

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Cubbit comes in 3 components:

  • One or more Storage nodes which includes their agent software running ontop of a linux system with direct attached storage.
  • One or more Gateway nodes which provides S3 protocol acces to the objects stored on storage nodes. Typical S3 access points https://S3.company_name, com/… points to either a load balancer, front end or one or more Gateway nodes. Gateway nodes provide the mapping between the bucket name/object identifier and where the data currently resides or will reside.
  • One Coordinator node which provides the metadata to locate the data for objects, manage the storage nodes, gateways and monitor the service. The Coordinator node can be a SaaS service supplied by Cubbit or a VM/bare metal node running Cubbit Coordinator software. Metadata is protected internally within the Coordinator node.

With these three components one can stand up a complete, geo-distributed/geo-fenced, S3 object storage system which the organization controls.

Cubbit encrypts data as it at the gateway and decrypts data when accessed. Sign-on to the system uses standard security offerings. Security keys can be managed by Cubbit or by standard key management systems.

All data for an object is protected by nested erasure codes. That is 1) erasure code within a data center/location over its storage drives and 2) erasure code across geographical locations/data centers..

With erasure coding across locations, customer with say 10 data center locations can have their data stored in such a fashion that as long as at least 8 data centers are online they still have access to their data, that is the Cubbit storage system can still provide data availability.

Similarly for erasure coding within the data center/location or across storage drives, say with 12 drives per stripe, one could configure lets say 9+3 erasure coding, where as long as 9 of the drives still operate, data will be available.

Please note the customer decides the number of locations to stripe across for erasure coding, and diet for the number of storage drives.

The customer supplies all the storage node hardware. Some customers start with re-purposed servers/drives for their original configuration and then upgrade to higher performing storage-servers-networking as performance needs change. Storage nodes can be on prem, in the cloud or at the edge.

For adequate performance gateways and storage nodes (and coordinator nodes) should be located close to one another. Although Coordinator nodes are not in the data path they are critical to initial object access.

Gateways can provide a cache for faster local data access.. Cubbit has recommendations for Gateway server hardware. And similar to storage nodes, Gateways can operate at the edge, in the cloud or on prem.

Use cases for the Distributed S3 storage include:

  • As a backup target for data elsewhere
  • As a geographically distributed/fenced object store.
  • As a locally controlled object storage to feed AI training/inferencing activity.

Most backup solutions support S3 object storage as a target for backups.

Geographically distributed S3 storage means that customers control where object data is located. This could be split across a number of physical locations, the cloud or at the edge.

Geographically fenced S3 storage means that the customer controls which of its many locations to store an object. For GDPR countries with multi-nation data center locations this could provide the compliance requirements to keep customer data within country.

Cubbit’s distributed S3 objects storage is strongly consistent in that an object loaded into the system at any location is immediately available to any user accessing it through any other gateway. Access times vary but the data will be the same regardless of where you access it from.

The system starts up through an Ansible playbook which asks a bunch of questions and loads and sets up the agent software for storage nodes, gateway nodes and where applicable, the coordinator node.

At any time, customers can add more gateways or storage nodes or retire them. The system doesn’t perform automatic load balancing for new nodes but customers can migrate data off storage nodes and onto other ones through api calls/UI requests to the Coordinator.

Cubbit storage supports multi-tenancy, so MSPs can offer their customers isolated access.

Cubbit charges for their service on data storage under management. Note it has no egress charges, and you don’t pay for redundancy. But you do supply all the hardware used by the system. They offer a discount for M&E customers as the metadata to data ratio is much smaller (lots of large files) than most other S3 object stores (mix of small and large files).

Cubbit is presently available only in Europe but will be coming to USA next year. So, if you are interested in geo-distributed/geo-fenced S3 object storage that you control and can be had for much cheaper than hyperscalar object storage, check it out.

Enrico Signoretti, VP Products & Partnerships

Enrico Signoretti has over 30 years of experience in the IT industry, having held various roles including IT manager, consultant, head of product strategy, IT analyst, and advisor.

He is an internationally renowned visionary author, blogger, and speaker on next-generation technologies. Over the past four years, Enrico has kept his finger on the pulse of the evolving storage industry as the Head of Research Product Strategy at GigaOm. He has worked closely and built relationships with top visionaries, CTOs, and IT decision makers worldwide.

Enrico has also contributed to leading global online sites (with over 40 million readers) for enterprise technology news.

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166: Greybeard talks MLperf Storage benchmark with Michael Kade, Sr. Solutions Architect, Hammerspace

Sponsored By:

This is the first time we have talked with Hammerspace and Michael Kade (Hammerspace on X), Senior Solutions Architect. We have known about Hammerspace for years now and over the last couple of years, as large AI clusters have come into use, Hammerspace’s popularity has gone through the roof..

Mike’s been benchmarking storage for decades now and recently submitted results for MLperf Storage v1.0, an AI benchmark that focuses on storage activity for AI training and inferencing work. We have written previously on v0.5 of the benchmark, (see: AI benchmark for storage, MLperf Storage). Listen to the podcast to learn more.

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Some of the changes between v0.5 and v1.0 of MLperf’s Storage benchmark include:

  • Workload changes, they dropped BERT NLP, kept U-net3D (3D volumetric object detection) and added ResNet-50 and CosmoFlow. ResNet-50 is an (2D) image object detection model and CosmoFlow uses a “3D convolutional neural network on N-body cosmology simulation data to predict physical parameters of the universe.” Both ResNet-50 and CosmoFlow are TensorFlow batch inferencing activities. U-net3D is a PyTorch training activity.
  • Accelerator (GPU simulation) changes, they dropped V100 and added A100 and H100 emulation to the benchmarks.

MLperf Storage benchmarks have to be run 5 times in a row and results reported are the average of the 5 runs. Metrics include samples/second (~files processed/second), overall storage bandwidth (MB/sec) and number of accelerators kept busy during the run (90% busy for U-net3D & ResNet-50 and 70% for CosmoFlow).

Hammerspace submitted 8 benchmarks: 2 workloads (U-net3D & ResNet-50) X 2 accelerators (A100 & H100 GPUs) X 2 client configurations (1 & 5 clients). Clients are workstations that perform training or inferencing work for the models. Clients can be any size. GPUs or accelerators are not physically used during the benchmark but are simulated as dead time, depending on the workload and GPU type (note this doesn’t depend on client size)

Hammerspace also ran their benchmarks with 5 and 22 DSX storage servers. Storage configurations matter for MLperf storage benchmarks and for v0.5, storage configurations weren’t well documented. V1.0 was intended to fix this but it seems there’s more work to get this right.

For ResNet-50 inferencing, Hammerspace drove 370 simulated A100s and 135 simulated H100s and for U-net3D training, Hammerspace drove 35 simulated A100s and 10 simulated H100s. Storage activity for training demands a lot more data than inferencing.

It turns out that training IO also uses checkpointing (which occasionally writes out models to save them in case of run failure). But the rest of the IO is essentially random sequential. Inferencing has much more randomized IO activity to it.

Hammerspace is a parallel file system (PFS) which uses NFSv4.2. NFSv4.2 is available native, in the Linux kernel. The main advantages of PFS is that IO activity can be parallelized by spreading it across many independent storage servers and data can move around without operational impact.

Mike ran their benchmarks in AWS. I asked about cloud noisy neighbors and networking congestion and he said, if you ask for a big enough (EC2) instance, high speed networks come with it, and noisy neighbors-networking congestion are not a problem.

Michael Kade, Senior Solutions Architect, Hammerspace

Michael Kade has over a 45-year history with the computer industry and over 35 years of experience working with storage vendors. He has held various positions with EMC, NetApp, Isilon, Qumulo, and Hammerspace.

He specializes in writing software that bridges different vendors and allows their software to work harmoniously together. He also enjoys benchmarking and discovering new ways to improve performance through the correct use of software tuning.

In his free time, Michael has been a helicopter flight instructor for over 25 years for EMS.