Category: Jason Collier

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173: GreyBeards Year End 2025 podcast

Well this year went fast. Keith, Jason and I sat down to try to make some sense of it all.

AI is still on a tear and shows no end in sight. Questions abound on whether we are seeing signs of a bubble or not, our answer – maybe. We see it in GPU pricing. in AI startup valuations, and in enterprise interest. Some question whether the Enterprise is seeing any return from investments in AI but there’s no doubt they are investing. Inferencing on prem with training/fine tuning done in neo-clouds, has become the new norm. I thought we’d be mostly discussing agentic AI but it’s too early to for that yet.

In other news, the real Broadcom VMware play is starting to emerge (if ever in doubt). It’s an all out focus on the (highly profitable) high end enterprises, and abandon the rest. And of course the latest weirdness to hit IT is DRAM pricing, but in reality it’s the price of anything going into AI mega-data centers that’s spiking. Listen to the podcast to learn more

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AI

GPU pricing is still high, although we are starting to see some cracks in NVIDIA’s moat.

AMD GPUs made a decent splash in the latest MLperf Training results and Google TPUs are starting to garner some in the enterprise. And NVIDIA GPUs are becoming less of a compute monster by focusing more with their latest GPU offerings on optimization for low precision compute, FP2 anyone, rather than just increasing compute. It seems memory bandwidth (in GPUs) is becoming more of a bottleneck than anything else IMHO.

But NVIDIA CUDA is still an advantage. Grad students grew up on it, trained on it and are so familiar with it, it will take a long time to displace. Yeah, RoCM helps but, IT needs more. Open Sourcing all the CUDA code and its derivatives could be an answer, if anybody’s listening.

Jason talked about AI rack and data center power requirements going through the roof and mentioned SMR (small modular [nuclear] reactors) as one solution. When buying a nuclear power plant is just not an option, SMRs can help. They can be trucked and installed (mostly) anywhere. Keith saw a truckload of SMRs on the highway on one of his road trips.

And last but not least, Apple just announced RDMA over Thunderbolt. And the (Youtube) airwaves have been lighting up with studio Macs being clustered together with sufficient compute power to rival a DGX. Of course it’s Apples MLX running rather than CUDA, and only so many models work on MLX, but it’s a start at democratizing AI.

VMware

Broadrcom’s moves remind Jason of what IBM did with Z. Abandoning the low end, milk the high end forever. If you want vSphere better think about purchasing VCF.

Keith mentioned if a company has a $100M cloud spend, they could save some serious money (~20%), going to VCF. But it’s not a lift and shift. Running a cloud on prem requires a different mindset than running apps in the cloud. Welcome to the pre-cloud era, where every IT shop did it all.

Component Pricing

Jason said that DRAM pricing has gone up 600% in a matter of weeks. Our consensus view is it’s all going to AI data centers. With servers having a TB of DRAM, GPUs with 160GB of HBM per, and LPDDR being gobbled up for mobile/edge compute everywhere is it any doubt that critical server (sub-) components are in high demand.

Hopefully, the Fabs will start to produce more. But that assumes Fab’s have spare capacity and DRAM demand is function of price. There are hints that neither of these are true anymore. Mega data centers are not constrained by capital, yet, and most Fabs are operating flat out producing as many chips as they can. So DRAM pricing may continue to be a problem for some time to come.

Speaking of memory, there was some discussion on memory tiering startups taking off with high priced memory. One enabler for that is the new UALink interconnect. It’s essentially an open source, chip-to-chip interconnect technology, over PCIe or Ethernet. UAlink solutions can connect very high speed components beyond the server itself to support a scale out network of accelerators, memory and CPUs in a single rack. It’s early yet but Meta specs for an OCP wide form factor rack was released in the AMD Helios OGP 72GPU rack that uses UALink tech today, More to come we’re sure.

Keith Townsend, The CTO Advisor, Founder & Executive Strategist | Advisor to CIOs, CTOs & the Vendors Who Serve Them

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.

Jason Collier, Principal Member Of Technical Staff at AMD, Data Center Solutions 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

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171: GreyBeards talk Storage.AI with Dr. J Metz, SNIA Chair and Technical Director, AMD

SNIA’s Storage Developer Conference (SDC) was held last week in CA and although I didn’t attend I heard it was quite a gathering. Just prior to the show, I was talking with Jason about the challenges of storage for AI and he mentioned that SNIA had a new Storage.AI initiatdze focused on these issues. I called Dr. J Metz, Chair of SNIA & Technical Director @ AMD (@drjmetz, blog) and asked if the wanted to talk to us about SNIA’s new initiative.

Storage.AI is a SNIA standards development community tasked with addressing the myriad problems AI has with data. Under its umbrella, a number of technical working groups (TWGs) will work on standards to improve AI data access, Just about every IT vendor in the universe is listed as a participating company in the initiative. Listen to the podcast to learn more.

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We started discussing Dr. J’s current roles at SNIA and AMD and how SDC went last week. It turns out, it was the best attended SDC ever and Dr. J’s keynote on Storage.AU was a highlight of the show.

The storage/data needs for AI span a wide spectrum of activities or workloads. Dr. J spoke on the lengthy data pipeline, e.g. ingest, prep/clean, transform, train, checkpoint/reload, RAG upload/update and inference to name just a few. In all these diverse activities, storage’s job is getting the right data bits to the right process (GPU/accelerators for training) throughout the pipeline. Inferencing has somewhat less of a convoluted data journey but is still complex and performance critical.

Te take just one component of the data pipeline checkpointing is a data intensive process. When training a multi-billion parameter model or, dare I say, multi-trillion parameter model with 10K to Million’s of GPUs, failure’s happen, often. Checkpoints are the only way model training can make progress in the face of significant GPU failures. And of course, any checkpoint needs to be reloaded to verify it’s correct.

So checkpointing and reloading is an IO activity that happens constantly when models are trained. Checkpoints essentially save the current model parameters during training. Speeding up checkpoint/reload could increase AI model training throughput considerably

And of course, GPUs and the power they consume are an expensive activity. When one has 1000’s to Millions of GPUs in a data center, having them sit idle is a vast waste of resources. Anything to help speed up accelerator data access could potentially save millions.

In the old days compute, storage and networking were isolated/separate silos of technology. Nowadays, the walls between them have been blown away, mostly by the advent of AI.

Dr. J talks about first principles, such as the speed of light that determines the time it takes for data to move from one place to another. These limits exist throughout IT infrastructure. But OS stacks surrounding these activities have spawned layer upon layer of software to do these actions. If one can wipe the slate clean, infrastructure activities can get closer to those first principles and reduce overhead

SNIA has current TWGs focused on a number of activities that could help speed up AI IO. We talked about SNIA’s Smart Data Acceleration Initiative (SDXI), but there are others in process as well. But SNIA has also identified a few new ones they plan to fire up such as GPU direct access bypass and GPU-Initiated IO to address other gaps in Storage.AI.

In today’s performance driven AI environments, proprietary solutions are often developed to address some of these same issues. We ended up discussing the role of standards vs. proprietary solutions in IT in general and in today’s AI infrastructure.

Yes there’s a place for proprietary solutions and there’s also a place for standards. Sometimes they merge, sometimes not, but they can often help inform each other on industry trends and challenges.

I thought that proprietary technologies always seem to emerge early and then transition to standards over time. Dr. J said it’s more of an ebb and flow between proprietary and standards, and mentioned as one example the ESCON-FC-FICON-Fabric proprietary/standards activities from last century.

As always It was an interesting conversation with Dr. J and Jason and I look forward to seeing how SNIA’s Storage.AI evolves over time.

Dr. J. Metz, Chair and Chief Executive of SNIA & Technical Director, AMD

J is Technical Director for Systems Design for AMD where he works to coordinate and lead strategy on various industry initiatives related to systems architecture, including advanced networking and storage. He has a unique ability to dissect and explain complex concepts and strategies, and is passionate about the inner workings and application of emerging technologies.

J has previously held roles in both startup and Fortune 100 companies as a Field CTO, R&D Engineer, Solutions Architect, and Systems Engineer. He is and has been a leader in several key industry standards groups, currently Chair of SNIA as well as the Chair of the Ultra Ethernet Consortium (UEC). Previously, he was on the board of the Fibre Channel Industry Association (FCIA) and Non-Volatile Memory Express (NVMe) organizations. A popular blogger and active on Twitter, his areas of expertise include both storage and networking for AI and HPC environments.

Additionally, J is an entertaining presenter and prolific writer. He has won multiple awards as a speaker and author, writing over 300 articles and giving presentations and webinars attended by over 10,000 people. He earned his PhD from the University of Georgia.

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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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164: GreyBeards talk FMS24 Wrap-up with Jim Handy, General Dir., Objective Analysis

Jim Handy, General Director, Objective Analysis, is our long, time goto guy on SSD and Memory Technologies and we were both at FMS (Future of Memory and Storage – new name/broader focus) 2024 conference last week in Santa Clara, CA. Lots of new SSD technology both on and off the show floor as well as new memory offerings and more.

Jim helps Jason and I understand what’s happening with NAND, and other storage/memory technologies that matter to today’s IT infrastructure. Listen to the podcast to learn more.

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First off, I heard at the show that the race for more (3D NAND) layers is over. According to Jim, companies are finding it’s more expensive to add layers than it is just to do a lateral (2D, planar) shrink (adding more capacity per layer).

One vendor mentioned that the CapEx Efficiencies were degrading as they add more layers. Nonetheless, I saw more than one slide at the show with a “3xx” layers column.

Kioxia and WDC introduced a 218 layer, BICS8 NAND technology with 1Tb TLC and up to 2Tb QLC NAND per chip. Micron announced a 233 layer Gen 9 NAND chip.

Some vendor showed a 128TB (QLC) SSD drive. The challenge with PCIe Gen 5 is that it’s limited to 4GB/sec per lane and for 16 lanes, that’s 64GB/s of bandwidth and Gen 4 is half that. Jim called using Gen 4/Gen 5 interfaces for a 128TB SSD like using a soda straw to get to data.

The latest Kioxia 2Tb QLC chip is capable of 3.6Gbps (source: Kioxia America) and with (4*128 or) 512 of these 2Tb chips needed to create a 128TB drive that’s ~230GB/s of bandwidth coming off the chips being funneled down to 16X PCIe Gen5 64GB/s of bandwidth, wasting ~3/4ths of chip bandwidth.

Of course they need (~1.3x?) more than 512 chips to make a durable/functioning 128TB drive, which would only make this problem worse. And I saw one slide that showed a 240TB SSD!

Enough on bandwidth, let’s talk data growth. Jason’s been doing some research and had current numbers on data growth. According to his research, the world’s data (maybe xmitted over internet) in 2010 was 2ZB (ZB, zettabytes = 10^21 bytes), and in 2023 it was 120ZB and by 2025 it should be 180ZB. For 2023, thats over 328 Million TB/day or 328EB/day (EB, exabytes=10^18 bytes).

Jason said ~54% of this is video. He attributes the major data growth spurt since 2010 to mainly social media videos.

Jason also mentioned that the USA currently (2023?) had 5,388 data centers, Germany 522, UK 517, and China 448. That last number seems way low to all of us but they could just be very, very big data centers.

No mention on the average data center size (meters^2, # servers, #GPUs, Storage size, etc). But we know, because of AI, they are getting bigger and more power hungry,

There were more FMS 2024 topics discussed, like the continuing interest in TLC SSDs, new memory offerings, computational storage/memory, etc.

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