Category: Hardware Infrastructure

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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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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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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.