There seems to be concern that Nvidia is going to slip on its delivery of its Kyber NVL144 racks, even as Jensen continues to promise that the roadmap is intact and on track. There’s a couple of interesting things here. Specifically, it’s not the production of Vera CPUs or Rubin GPUs that’s in question here, it’s the production of the NVL144 rack and the additional interconnects this entails.

Firstly, this gives competitors like AMD and their Helios rack or Google’s TPU architecture a bit of leeway to make inroads into Nvidia’s market. Critically, what’s going to be important here is not the performance of a single GPU but the scale out capability of the entire rack.

Which brings me to my second point. 

The first supercomputers, such as the Cray One used its own integrated circuits designed specifically for it, the evolution and market dynamics of integrated circuits (of which you CPU is one) meant that later supercomputers, such as El-Capitan, were built on integrated circuits provided by companies dedicated to building them such as AMD and Intel. It was companies such as Dell, or HPE that then took those CPUs coupled them with cooling, networking, racks etc and created a supercomputer. Cray disappeared and became part of HPE.

Nvidia is now creating not only the silicon, but the entire rack. I’m not sure if that’s the silicon manufacturer eating the ODM but it does mean we’re returning to an era where the same company is creating the silicon, the interconnects, the rack design and even part of the cooling infrastructure. I wonder how this plays out.

Talking about interconnects, Marvell has released its Teralynx 100 kit.

TSMC is taking its massive profits and investing not only in additional capacity but also the USA. I guess if you’re biggest customers are spending like crazy now is the time to expand.

And coming back to what the ODM now do, they seem to be turning to integrating other accelerator options, specifically quantum, into their offerings with both HPE and Dell offering something in this space and placing quantum computing firmly in the realm of your next accelerator as opposed to your next data center.

All the News in Depth

Cloud and vendor releases for Supercomputing & HPC (and AI too if you change the filters)

TSMC invest further in the USA and plans for greater expansion, or see Next Platform more their more in depth take.

Where’s that Kyber NVL144 gone?

Marvell ups its game in supercomputing interconnects

Quantum is firmly establishing itself as the next type of accelerator

Not quite the point, but this is good example of hybrid AI/Classical workflows establishing themselves

HMx Labs Updates

HPC is like Poker. You always need to know who’s bluffing

HPC is like Poker

My funniest conversation from HPC Club: HPC is like poker!

Flux by HMx LabsHamza

Compute futures and markets I suspect would be amazing for HPC and supercomputing. We have them. Sort of. Sadly not in any way that’s actually useful to the people that need to consume the compute

Compute Futures are Here. Sort Of.

We now have compute futures, something I’ve been wanting for year. But they suck.

Flux by HMx LabsHamza

Off Topic

Layered silicon!

AI Rewrites can work?

Open source is getting harder and harder

Want some cheap GPUs? Almost.

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I have been talking about compute futures for a long time. Since before it was trendy. Before AI drove compute demand through the roof. World famous financial exchanges and back street prediction markets alike are now offering compute future. And I am… underwhelmed.

Why? Surely, I’ve been wanting a way to trade compute and now I finally have it, right? Wrong.

What we have today is the ability to speculate about the price of compute. I understand this is one side of what financial markets offer. It’s not the side that I care about. What I am, and always have been, looking for is the side of financial markets that are used by people that actually care about using the underlying commodity. Not just betting on derivative.

Details are rather scant on Kalshi’s new compute futures and given that half the information I’ve read about it doesn’t even understand the difference between a futures contract and a forward I’m not going to hold my breath that this becomes something useful for anything other than speculation.

If what you really care about is actually buying and consuming compute and want financial markets for all the benefits they bring to that, we still have several unsolved problems that everyone is trying to sweep under the rug. Who care about physical delivery or settlement if all you want to do is provide a cool new replacement to meme coins.

I don’t need to provide the world with another thing to bet on. I want to provide something that has real utility for those us that need to use that compute.

This means solving problems like delivery. No one that is serious about consuming large amounts of compute today is buying a futures contract on a prediction market because their use of that compute will be tied to some very specific APIs and network connectivity. We have nothing even approaching a standardised way to consume compute let alone arranging actual delivery.

It also means solving the fungibility problem. The performance of the same cloud VM type across regions can vary by over 20%, the difference in performance across vendors is even greater. A compute future for a H100 is a completely nonsensical product if you actually care about using that compute and not just speculating on the price of it. And no, the solution here isn’t to make everything homogenous. It’s to know how to have the right exchange rate. An exchange rate that varies by workload.

I’m not just ranting about this either. These are problems that not that hard to solve. We are doing our little bit (see ORB and FLOPx). Doesn’t make it any less frustrating.

HPC is like poker.

I don’t recall how exactly this conversation came about, but that was the opening claim. As per the rules of HPC Club, I won’t disclose who said it, but that person is welcome to claim credit here 😄

“HPC is like poker. You start of thinking it’s about the cards but then realise it’s about the people.”

The idea being, of course, that with HPC it’s not really about the hardware specs, how many GPUs you have or the scheduler you run. It’s about understanding and enabling the users to run the workloads they need to. That wasn’t my response though.

“What, you mean  you need to able to figure out who’s telling the truth and who’s bluffing when they ask you for 256 GPUs?”

I think this bit just as true!

P.S: HPC Club is great to pick up free swag too 😁 🥳

The supercomputing theme for last week seems to have been heterogeneity. Which is kind of fun because we talked about that at HPC Club last week too!

We seem to be heading towards an ever increasing diversity of compute options with both Meta and DeepSeek announcing their own accelerator for AI last week. This comes on top of Meta having already announced its intention to tape out its own ARM based CPUs earlier this year too. Being an AI player or a hyperscaler in the 20s requires your own silicon it would seem. I wonder how long that will last.

I guess it’s just as well then, that startup ZML launched its hardware agnostic LLMD that promises to run your inference across five different options including CUDA (of course), ROCm and Google’s TPUs. Interestingly, they also chose to target Apple Metal for their initial release. I guess they see a chunk of LLM inference heading to local use and this being valuable in the consumer as well as data centre space?

In many ways, the situation with Nvidia and CUDA today reminds me of Intel and MKL in the 2000s. If you were locked into the Intel + MKL ecosystem, it may have been a little painful over the last five years or so watching AMD surpass Intel in both performance and cost per calc. If you experienced that level of lock in, that is no doubt driving some of the desire we see today to gain independence from Nvidia and CUDA. I think that’s great. I would love to see an open ecosystem where it is easy to avoid vendor lock in. Let’s not kid ourselves though.

Cross platform compute is not easy and it’s not cheap. While you may be kicking yourself if you’re locked into Intel CPUs today, you were in a fantastic place for 20 odd years. You might be surprised to hear me advocating for voluntary vendor lock in. And I’m not really. What I am saying is to make your choices carefully. The alternative is not free and sometimes, provided you understand what you’re letting yourself in for, the vendor lock in can be preferable.

If you missed HPC Club last week and want to catch up on what the team at Lawrence Livermore National Lab had to say then you’ll be glad to hear you can watch it online, the details for that and the rest of the supercomputing news from last week in depth is below.

All the News in Depth

Cloud and vendor releases for Supercomputing & HPC (and AI too if you change the filters). Google has a new network optimised, Intel Xeon Emerald Rapids based, VM type with the C4N.

Meta wants to make its own accelerators too now, having already decided that it needed its own ARM based CPUs earlier this year.

DeepSeek, with possibly a stronger reason, has also decided to make its own

ZML’s accelerator independent LLMD goes live giving us a fighting shot at being able to run our code on at least some of these accelerators without lots of rework and their own press release.

You know when there’s an outage how it’s always DNS? Well, HPC has its own rule, it’s always the data. Anyone that’s been working in supercomputing for a minute already knows this of course but it’s fun to see all the AI kids learning that lesson too.

HMx Labs Updates

If you missed HPC Club last week but really want to know what the clever people at LLNL are up to with HPC and agentic workflow, you can watch their presentation online.

HPC Club: Supercomputing, Agentic AI & Finance with LLNL

That’s a wrap on HPC Club with Lawrence Livermore National Lab. Amazing presentation from Vanessa and Dan on Flux Framework, Agentic workflows tied in with their use in financial services.

Flux by HMx LabsHamza

We’re still hiring

Hiring: HPC Engineer

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Flux by HMx LabsHamza

Off Topic

Linus Torvalds and The Primeagen on AI written code

I like Ed Zitron but this feel pretty spot on to me

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One of the things I love most about HPC Club is its ability to enable cross domain and interdisciplinary conversations. And Vanessa and Dan exemplified this perfectly by turning their experience operating El Capitan, one of the world’s largest supercomputers, to financial services at our last event.

Financial services and academia have historically been seen as distinct. The very personification of high throughput computing versus classical high performance computing. But they’re converging.

And the financial services community may also be one of the last to admit it.

Most people still have the impression that financial services run only embarrassingly parallel compute (P.S: I really don’t like that term). This was true in 2000. Today the answer is far more nuanced. From 2008 or so with the increase in portfolio risk analytics a lot the demand for compute in banking has been made to fit the mould of HTC and the systems that were already available. If someone had handed the quant team a MPI manual, things would look quite different!

The arrival of LLM inference has served only to increase that pressure. Topological awareness and inter node communication are starting to become as important as high throughput rates.

We are also operating in a world that is increasingly heterogenous. The introduction of CUDA meant dealing with CPU vs GPU compute. Although it’s been almost twenty years, honestly even this level of heterogeneity is not well solved today. Yes, it works. Yes, it can be well optimised. Not it’s not commoditised and easy to do. It should be.

Not only do we now have an increased variability in CPU architectures with the large scale adoption of ARM, RISC-V on the horizon and AMD taking centre stage from Intel in the x86 world, we are looking at an increasingly varied accelerator space. This means not only GPUs alternatives from AMD, Google’s TPU, wafer scale accelerators from Cerberas to name a few but also quantum and neuromorphic computing muscling in. These challenges are not unique to either academia or finance.

It's against this backdrop that Vanessa and Dan from Lawrence Livermore National Lab presented Flux Framework and the use of Agentic AI in HPC and supercomputing. All wrapped up with is application to a domain that new to them: Financial services.

Like I said, a perfect example of cross domain collaboration.

The full presentation is now available on YouTube. Links on the HPC Club website.

HPC Club is today!

Quick reminder that it is taking place in Farringdon and not Canary Wharf like most of the previous events. The nearest station is Farringdon which is on the Elizabeth and Metropolitan lines (both of which have air conditioning!).

We are sold out but there is a wait list. We occasionally get a few people drop out on the day so do sign up to the wait list if you wanted to come along. If you really need to be in the room and forgot to book your spot, give me a shout 😆

See you this evening folks! 

Lessons from LLNL in Supercomputing & AI - July 2026 - HPC CLUB

HPC Club event on CPUs, GPUs and silicon diversity. Join us for an evening of discussion and networking with industry experts. 26 November 2025 @ 17:00, Canary Wharf, London.

HPC CLUB

You’d have to have your head buried pretty deep in the sand to have not heard the news that Meta is contemplating becoming the newest neocloud. Are they young enough to be a neocloud? Or do they just get to be a regular cloud selling a higher proportion of GPU compute? 🤔

That’s not the interesting bit. The interesting bit is watching for what happens next. We’ve already seen SpaceXAI (whatever it’s called this week) decided to resell their compute capacity to Anthropic and Google. Those deals happened pretty quickly, holding up the narrative that since approximately the end of 2025 there has been a strong demand (and increasing prices) for GPU compute. How quickly, and to whom, any Meta’s spare capacity gets sold may well prove to be a bell weather for how deep the demand for GPU compute (and by association, AI) really is. All against a backdrop of reduced tokenmaxxing and increased adoption of open weight models.

It goes without saying, I really don’t envy anyone having to make the decision on new hardware (especially GPU) purchases right now. At least in terms of trying to guess which way prices are going to go (even if the short-term trajectory is still upwards). Just to make it even harder though, there’s a very valid and interesting question around how much longer we will even need GPUs.

We have a few HPC heavyweights talking about a pattern that plays out over and over again in computing and specifically with CPUs. As a type of compute becomes more prevalent, the ability to perform it (in hardware) on the CPU follows. We saw this with floating point math, media encoding and cryptography, it’s only natural that mixed precision vector math follows right? We can hope. And it’s not like there isn’t prior art in the supercomputing world. I’m looking at you Fugaku and LineShine.

Right now, the demand for silicon seems so insatiable that even the master of supply chain economics, Apple, has put up prices. South Korea is doubling down with large investments into new plants so we can hope that eases supply issues. In about 5 years.

The real constraint to datacentre growth though has always been the utilities it is plumbed in and hooked up to. Interesting then, to see the impact its having on consolidation in the utility markets. You think something looks mature and highly optimized and then the demand increases tenfold overnight, I guess that wakes people up.

Lastly (and most importantly 😉), its HPC Club on Wednesday 🎉. I think we have 2 places left which is pretty unusual so close to the event. See you there!

All the News in Depth

Cloud and vendor releases for Supercomputing & HPC is a bit delayed this week. Our application for tracking seems to have run into a bug. That’ll teach me to vibe code it. Back soon!

Meta to become the latest neo cloud or the in depth Next Platform take

Of course we may not need GPUs too much longer

CPUs or GPUs, assuming we keep demanding the compute perhaps South Korea will be able to produce it or at least the memory that goes with it

Its really not secret that datacentre growth is constrained by (and has been forever) utilities. Interesting to see them starting to consolidate even further though

HMx Labs Updates

HPC Club is this week!

HPC Club – 1 Week to go

One week to go and only a handful of spots left.

Flux by HMx LabsHamza

Still hiring

Hiring: HPC Engineer

We’re looking for another HPC software engineer! Know someone that might be interested? Fancy pointing them my way please? This video was brought to you by the letters H, M and X and the number 42. Have a good weekend everyone. 0:00 /0:03 1× Apply on LinkedIn

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Off Topic

Not exactly off topic for this week I guess, Ed Zitron on Meta selling compute

I’m not sure why Dario’s comments from some time ago are being discussed so heavily right now? Well I have some ideas but that’s a whole another 500 words so I’ll keep them to myself.

Besides, Alberta is funnier than I am so just watch this to get some idea.

And just as I start thinking we need to do something in quantum space it gets all weird

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ISC is over. But now that you’ve had a few days to rest, the conversations in your head are still spinning. Need an outlet? Come along to HPC Club in a week. 8 July to be precise.

When we first announced this edition of HPC Club with Lawrence Livermore National Lab presenting I think it was pitched as the owners of the world’s largest supercomputer, El-Capitan. Unfortunately, as of last week they have lost that title, being pushed down to number 2 by LineShine. It makes neither Vanessa nor Dan any less capable, but it takes the marketing sheen off things a little. Not that any true HPC NErD would care right?

Apart from being home to the number two supercomputer in the world, LLNL is also the birthplace of Slurm, one of (if not the most) widely used schedulers. It is also birthplace of Flux, the new scheduler that is run not only by El-Capitan but several other DoE supercomputers and is seeing increasing adoption now that it has been donated to the High Performance Software Foundation (a part of the Linux Foundation). Want to speak to the people who created it? Not only about Flux but also its integration and how they’re using AI workflows?

As if that wasn’t enough, we’ll also have Man Group presenting on the challenges they have faced in AI adoption. In case you haven’t heard of them, Man Group is a hedge fund and places like that don’t run on hope and hype so if you want to see what real usage looks like, come along and find out.

As of this morning there were only 10 places left, so don’t hang about. Please do sign up even if the event becomes wait listed, if there’s enough demand, we’ll see what we can do about extending the capacity.

Lessons from LLNL in Supercomputing & AI - July 2026 - HPC CLUB

HPC Club event on CPUs, GPUs and silicon diversity. Join us for an evening of discussion and networking with industry experts. 26 November 2025 @ 17:00, Canary Wharf, London.

HPC CLUB

HPC Club: Supercomputing & AI with LLNL & Man Group · Luma

With the current hype, it can be confusing about what AI can really do, and potential benefits for science and the financial industry. For this HPC Club, we…

HPC Club

It would be hard to cover what was important in supercomputing and HPC last week without talking about ISC 26. So, this edition of The SC brings blends the news from Europe’s largest supercomputing conference with everything else that happened last week.

The opening keynote talked about something I’ve been harping on about for almost two years at this point. Homogenous supercomputing is dead. Well maybe not quite yet but the future is absolutely heterogenous. Whilst two years ago that meant having to deal with multiple CPU and GPU manufacturers and knowing which one to pick, today that means a range of possible CPU architectures, GPU architectures and other accelerators including quantum and neuromorphic computing. The framing here, of quantum as another accelerator, in itself was quite interesting. And quite correct.

Quantum computing dominated not only the opening keynote but in many regards the conference itself. Where last year storage vendors took the most prominent position on the conference floor and competed for attention, this year (apart from Nvidia of course) that honour went to quantum computing vendors who dominated by number if not by placement.

My conversations (with multiple vendors) were all of course very optimistic and pointed to a bright future for quantum. Unlike AI however, it is far harder to draw the line on where reality ends and the hype starts with quantum computing. My own interests lie with its applicability to financial risk analytics and while many references were made to papers where this has been shown as feasible, I think this is an area that will need some actual work to be done (by us). Watch this space, I guess? If there’s enough interest, I guess we will have some work to do in separating the wheat from the quantum chaff.

Non ISC news about Microsoft’s topological qubits with Marjorana 2 seemed to echo all my conversations at ISC. i.e. they don’t exist. In fairness to Microsoft, I guess those conversation were all with their competitors in this space 😁

The release of the Top500, even with a new machine taking the number one spot this year, hardly featured in my conversations at all. Perhaps this speaks to the decreasing relevance of the Top 500? The Top500 itself is changing too with control passing over to ACM. I wonder if this will improve things.

China’s LineShine took the top spot, displacing El Capitan which had held it since November 2024. The first thing that stuck me about this was that LineShine had even bothered to submit to the Top500. It has been a number of years since China had stopped participating over increasing tensions between it and the USA. The decision to participate now, and with a CPU (ARM with HBM, quite interesting) only system I think speak volumes.

Overall, much like last year, the conference itself didn’t feature any new releases from vendors. It was no longer focused on attempting to be relevant in the wider AI news cycle either and the main floor felt less like a plumbing trade show (though there were still suppliers of liquid cooling solution present) than last year too. AI did still feature heavily but more in its application within HPC communities and the adoption of technology developed for AI into scientific computing. 

Which segues nicely into my next point. The keynotes, presentations and talks all feel very much like a community focused on science and academia. This is quite distinct from the highly commercial nature of the main show floor, and I felt that there’s a trick missing to build a better bridge here. To be clear, I don’t mean letting vendors take over the talks. That would be terrible. I do mean having more commercial users of supercomputing presenting and sharing ideas. I know this is always difficult to achieve but that just makes it all the more valuable. Supercomputing is so much bigger than just science and academia, we need to include more of those people in the conversation.

In other non ISC related news, OpenAI and its partnership with Broadcom has a name for their AI inference accelerator, Jalapeno. There seems to be little detail about it beyond this though.

Lastly, for the finance nerds like me, QuantMinds London is now open for booking with reduced prices till early August.

All the News in Depth

Cloud and vendor releases for Supercomputing & HPC (and AI too if you change the filters)

New Top500 with a new machine at the number 1 spot

and the Top500 itself hands over control to ACM

It seems like everyone is designing their own silicon now and OpenAI doesn’t want to be left behind. An Ed Zitron podcast claimed this was another wafer scale design but I don’t see any other claims of that from OpenAI themselves.

Calling this one out explicitly even though its in the release notes above… making HPC easier for the people that need it is something I think we’ve never been good enough at as a community… if AI really can solve that problem, then great

Does Microsoft’s Topological approach to Quantum actually produce any qubits? No one at ISC seemed to think so either.

EPCC gets a new supercomputer

Quant Minds booking now open

HMx Labs Updates

Last few spots left for HPC Club

HPC Club 08 July – Reservations Open

Want to come along to HPC Club on 8th July? Better book your spot now

Flux by HMx LabsHamza

Still not filled this role

Hiring: HPC Engineer

We’re looking for another HPC software engineer! Know someone that might be interested? Fancy pointing them my way please? This video was brought to you by the letters H, M and X and the number 42. Have a good weekend everyone. 0:00 /0:03 1× Apply on LinkedIn

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Off Topic

I’ve previously mentioned superconducting chips, how about diamond cooled instead?

Will OpenAI IPO this year after all?

Nightmare Eclipse strikes again

The surveillance state takes another step forward

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ISC26 kicks off in Hamburg today! The AI news has even been kind enough to have a small break in its insane pace so that everyone can talk about supercomputing instead 🎉 My worries about the lack of interest are also completely unfounded it seems with early numbers showing that the conference already has more registered participants than last year. HPC Wire has a few themes to look out for, the last of which is quantum computing…

In what felt like a scripted TikTok meme, but I can assure you wasn’t, as I sat in the barber’s chair yesterday, his opening conversation was about how much money he’s made on D-Wave and IonQ stock. I kid you not. Knowing what my day job is, he then proceeded to ask for advice on which quantum computing company to invest in next and which company would win. Have we hit peak hype for quantum already? Or is it genuinely poised to be the next useful thing? I don’t know but I think a lot of the plumbing that quantum computing uses may end up being more valuable (short term) than the quantum computing itself if the plans to use superconducting conventional computing play out.

In less funny but more important news, LLNL & co have selected Intel’s spin off Cornelis Networks for the Lynx supercomputer over the more traditional Infiniband in an announcement no doubt perfectly timed to allow Cornelis to talk about this at ISC. Nicely played. I guess this shouldn’t be too surprising in many regards as they were already using AMD APUs for El-Capitan and don’t seem averse to bucking the AI trend. Good on them!

Fancy over 80,000 CPU cores in a rack? Liquid cooled of course. Now you can with HPE Cray’s new offerings based on AMD’s Venice CPUs. With SSDs on coolers too it looks like a genuinely interesting design. I guess you better start planning for higher power consumption compute dense racks regardless of if you’re adopting GPUs or not. Even with Turin our testing showed 2KW per 1U sled theoretically giving you100KW fully loaded racks. Without a GPU in sight! eeeek.

The FP64 vs FP8 debate is getting lively post the “FP8 is All You Need” paper (see, we can’t even name research papers without a nod to the one that kicked off the current AI boom!) and its subsequent presentation at TPC26. My gut feel here is that FP64 will remain in demand and relevant though I’ll admit I have little to back that up beyond the overall tone of the conversations I’ve had in this space.

There’s another important conversation I think we should be having (maybe at ISC too?) which is around the future of open source software. I think this is even more important for the HPC and supercomputing community than for software in general and I suspect most of us are burying our heads in the sand around it.

See you in Hamburg! 

All the News in Depth

Cloud and vendor releases for Supercomputing & HPC (and AI too if you change the filters)

ISC is go with more attendees than last year already signed up.

HPCWire has a few topics to watch for

LLNL Picks Cornelis Networks for its Lynx supercomputer.

80k + cores in a rack with AMD Venice

FP8 or FP64? Which side of the argument do you fall on?

Shrinking Data Centres and using quantum computing plumbing for conventional compute

HMx Labs Updates

I think open source software is more important now than it has ever been

FOSS in the Age of AI

How do we solve for open source software in an age of AI. I don’t know but I thought I’d share some half baked ideas anyway.

Flux by HMx LabsHamza

A little out of date now and is it turn out, rather unfounded!

ISC? Where’s the Chatter?

This time last year, social media feeds and even industry headlines were filled with what’s to come at ISC. This year you’d barely even know it’s happening.

Flux by HMx LabsHamza

Off Topic

Modern day Luddites. This one is worth a watch, and I do mean watch as opposed to just listen as I first did via Apple Podcasts.

Linus Torvalds compares compilers to AI generation

Will vibe coding be the worst mistake we ever make as an industry?

Ed Zitron said something really interesting in this one. Like Ed, the praise heaped upon me by code generating genies rather rolls off my back. I don’t place any weight on it. I’d go so far as to say I tend to completely ignore it. I assumed everyone did this. Apparently not? Et tu brut? Also, the overall point by Cal Newport around both doom trolling and the backlash to it is very on point.

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I don’t talk about politics. I don’t post random, meandering, half baked thoughts. But its Friday and I’m going to do both today. Specifically, about Fable 5, open source software and AI more generally.

A common reaction I’ve seen to the US government’s restrictions placed on Fable 5 is an increased call for sovereign AI. While, especially outside the USA, that might pass the sniff test, it does feel a little like swapping Coca Cola for Pepsi. I’m sure the purveyors of anything sovereign AI/ hardware/ compute related will push this answer but its incomplete at best and deliberately misleading at worst.

Firstly, this assumes that USA based entities are perfectly happy with suddenly losing access to technology. I suspect that’s not the case. And this is your first clue that the answer doesn’t lie in further entrenching notions of an AI race with control granted to nation states and their proxies.

I’m not going to even pretend I know where the answer does lie. I do not. I will refer you back to my opening line.

However, for a long time, a sensible yin to the yang of proprietary software has been FOSS, free and open source software. The Linux to your Windows. The HT Condor to your IBM Symphony. Whilst it was nice that its free as in free beer, what has always been more important was that it was free as in available to inspect and change and use without restriction.

It’s that last part that is a clue to the right solution.

Unfortunately, FOSS has been having a rough time of late. Even before AI snuck into the party.

Whilst the XKCD meme of the internet being held together by one unpaid programmer in Nebraska is undoubtedly true, it is also true that that huge proportion of open source software today is paid for by commercial entities. As a result, we’ve seen several examples of “rug pull” license changes undermining confidence in open source software. Recent developments such as Malus for AI washing open source complicate things even further.

It would appear that without a solid commercial incentive, there is little reason to pursue anything open source, let alone anything as challenging as an alternative to Fable 5. P.S. open weights != open source. Though it’s a start, I guess. 

So how do we fix this? I still don’t know, but I do know that it has to come down to you and I. Big tech won’t fix it for us. An AI “race” and counting on other nation states with their own agendas won’t fix it either (though it might provide some short-term alternatives).

Even the “worst” companies have “good” people working in them. It’s down to us, you and I, to think of and provide use cases and justifications that can align with the corporate vision but produce ancillary benefits. We managed with projects such as Kubernetes.

FOSS becomes both harder and more important in an age of AI. I don’t know how we do it. I just know that we have to.

Feel free to tell me I’m wrong? Or grab me for a coffee to talk about it at ISC.

It’s ISC in Hamburg next week. Not that you’d know it from your industry press headlines or your social media feed. This time last year people were busy announcing that they’ll be attending, asking who else is going and arranging meetings. Many of the bigger companies were circulating invitations to parties and private meetings.

So far, this year, you’d barely even know ISC is happening in a week!

I know there’s a lot going on in the world of AI but even so I’m a little surprised it has taken over the conversation to this extent. We don’t even have HPC trying to muscle in on the AI conversation and position itself as relevant anymore. There seems to be more of a resignation and acceptance that HPC is now a user of AI like everyone else.

So, who’s going? I somehow doubt I will turn up to find an empty conference floor. Though it does perhaps feel a little more academia focused this year than it did last (looking at the presentations at least).

And if you can’t make it, come and catch up on all the gossip at HPC Club a week or so later instead 😉

Welcome to your rebranded and updated weekly roundup of news on all things supercomputing with a side of my own opinions. The old name bore little resemblance to what I actually talked about so it’s time for an update!

As a quick reminder, I tend to cover the bits of supercomputing and HPC that are important to people working in the field. The focus is generally more on the tech rather than what it can do. Less drug research and weather simulation and more CPU architectures and schedulers if you see what I mean. Though there might sometimes be a twist of finance thrown in. Sorry, can’t help myself! 

Strictly no AI hype! Frankly AI news is so well covered everywhere else, I often gloss over even the supercomputing relevant bits on the assumption you’ll have seen that elsewhere.

First up, its ISC next week though you’d never know it! AI has so completely taken over any form of news cycle, social media feed and even in person conversation that it barely registers. Is anyone even going? 😁 I note the hyperscalers are all notably absent from the sponsor list again.

Last week’s news was quite ARM flavoured. ARM has teamed up with Supermicro as resellers/ system builders of their first CPU. If you’re confused, see my previous take on ARM creating their own CPU [2]. Short version, though, I think it puts them in a much more complicated position. That doesn’t seem to have bothered AWS, who released version 5 of Graviton [3], their ARM based CPUs last week, currently only available in the M flavour (memory optimised) VMs but I’m sure we’ll see the C and R versions pop up shortly.

AMD isn’t taking this lying down though, with claims that its latest “Venice” generation of CPUs outperforms Nvidia Vera ARM based CPUs at rack scale [4]. I am so looking forward to getting my hands and on and benchmarking some of these myself.

Weka (a storage vendor) says they have a way to boost inference performance without buying more GPUs [5]. Not a great surprise really, I can’t remember the last time I had a supercomputing problem that was actually compute and not IO bound!

We still have a few places left for HPC Club so if you’ve not booked your place yet, I wouldn’t hang about [6] or  go here for the booking link[7] for those of you behind corporate firewalls that hate anything that doesn’t look like a boring vendor site (and some that do).

All the News in Depth

Cloud and vendor releases for Supercomputing & HPC (and AI too if you change the filters).

[1] ARM teams up with Supermicro for its new CPUs

[2] and my own previous thoughts on ARM making its own CPU

[3] AWS gives us new Graviton 5 ARM CPUs

[4] AMD pushes back on the performance claims of Nvidia’s ARM based Vera CPUs

[5] Weka boosts inference performance

HMx Labs Updates

[6] Details and tickets for HPC Club are now available

[7] Direct link to book if your corporate firewall hates fun

HPC Club 08 July – Reservations Open

Want to come along to HPC Club on 8th July? Better book your spot now

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We’re expanding FLOPx to be more useful for more of you, but to do that we need to know which benchmarks you care about

What Benchmarks Do You Care About?

We’re integrating new benchmarks into FLOPx. What data would you like to see?

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Still hiring for a HPC Engineer (but we’ve filled the marketing / bus dev role)

Hiring: HPC Engineer

We’re looking for another HPC software engineer! Know someone that might be interested? Fancy pointing them my way please? This video was brought to you by the letters H, M and X and the number 42. Have a good weekend everyone. 0:00 /0:03 1× Apply on LinkedIn

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Off Topic

This bit is new. Not supercomputing related but probably adjacent in some form or another, just things I’ve come across that I think are worth your time.

Has AI solved software? Not so much. I certainly still can’t get it to solve the more complex HPC adjacent problems I throw at it (at the systems as opposed to node level stuff).

Creator of OpenCode on Vibe Coding. He has some really interesting take and I found myself agreeing with a lot more than I expected for someone working inside the AI machine.

Creator of Zig. I’ve never used Zig, I don’t currently have a use for Zig. I still found this very interesting and was surprised how much I had in common with him in terms of his philosophy.

Know someone else who might like to read this newsletter? Forward this on to them or even better, ask them to sign up here: https://cloudhpc.news

In my last article, I introduced seven AI analyst personas that run against our Waltz enterprise architecture repository.

Each has a distinct lens: a CTO persona watching for infrastructure obsolescence, a Migration Architect hunting dependency cycles and shared-database cohorts, a TOGAF analyst spotting capability redundancy and standards drift, an Auditor reviewing the other six for agreement and contradiction. Each runs on a schedule, producing structured findings and recommending actions an architecture team can take.

A question I received afterwards stuck with me:  How do you stop this becoming another report nobody reads?

The honest answer is that the first version came surprisingly close to doing exactly that.

The findings were often accurate. But accuracy alone did not make them useful. The system still produced too much repetition, inconsistent prioritisation and occasional moments that made users question whether they could trust it.

Here are six lessons from improving it. Although the examples come from an enterprise architecture repository, most apply to any system in which AI continuously watches data and asks people to act on what it finds.

1. Let the model rediscover problems. Let the application remember them.

My first instinct was to tell each agent: “Do not report anything you already reported last week.”

Sounds reasonable, but it makes the model responsible for lifecycle state that the surrounding application should own.  An anti-pattern.

A better approach is to let the agent report what it sees on every run.  Assign each finding a fingerprint based on its structural identity:

·      which persona reported it

·      the finding category

·      the exact repository entities involved

When the same problem is observed again, it produces the same fingerprint. The system increments an observation count and can refresh the explanation, rather than creating another finding.

The wording can change without changing the identity of the problem.

This is also what makes dismissal meaningful. A user dismisses the underlying finding, not one particular version of the prose. When the agent notices the same issue again, it remains dismissed.

The practical lesson: models should detect; applications should remember.

2. Calibrate severity with contrasts, not adjectives.

Initially, the agents were asked to classify findings as low, medium, high or critical.

Nearly everything became critical.

The model treated any breached threshold as critical by default. It was not consistently considering who needed to act, how quickly they needed to act or whether an immediate response was practical.

Longer definitions did not solve this. Contrasting examples did.

For example:

“A single end-of-life production database supporting six named applications may require urgent intervention.”

Compared with:

“Two hundred applications missing an annual attestation represent a substantial programme of work, but not necessarily 200 separate emergencies.”

The important distinction is not simply the number of affected entities. It is the combination of impact, urgency, concentration of risk and the type of response available.

Once the personas were given contrasting cases, their severity ratings became much more useful.

The practical lesson: calibration examples often communicate judgement better than abstract definitions.

3. Never use generated prose as your navigation model.

The agents include entity references in their explanations. An early version of the interface turned references such as id=875 into application links.

Unfortunately, 875 was not always an application. It might have been a database, a capability or another type of repository entity.

The result was links to the wrong page, or to pages that did not exist.

That is worse than showing no link at all. A missing link is inconvenient. A confidently wrong link makes the entire finding feel unreliable.

Navigation is now driven exclusively by the structured entity references attached to the finding. The entity type and identifier are explicit, and the display name is resolved against the live repository when the finding is viewed.

References that appear only in generated prose are treated as text, not as ground truth.

The practical lesson: use language for explanation, use structured data for identity and navigation.

4. Reject malformed output without abandoning the run.

AI-generated structured output will occasionally be wrong.

A persona may return an unsupported category, omit a required field or provide an entity reference in the wrong format.

The first implementation treated this as a failed run. 

The better approach was to make validation part of the conversation with the model.

When a finding fails schema validation, the validation error is returned to the persona as feedback. The persona can then correct the finding and submit it again during the same run.

For example, a persona might return a severity of urgent when the schema only permits low, medium, high or critical. Rather than failing the run, we hand the error back — “urgent is not a valid severity; choose one of low, medium, high, critical” — and the persona resubmits with a severity of high.

This gives us both sides of the contract:

·      strict validation at the system boundary

·      a forgiving correction loop inside the run

We do not silently accept malformed data, but we also do not throw away an otherwise useful analysis because of one repairable mistake.

The practical lesson: be strict about what enters the system and helpful about how the model can correct it.

5. Measure consistency before tuning prompts.

Prompt tuning is very easy to do by instinct.

A wording change appears to produce a better result, so it is declared an improvement. But the same change may also make the agent less predictable or cause previously reliable findings to disappear.

Before further prompt tuning, I introduced a simple consistency test.

Each persona runs twice, back-to-back, against unchanged repository data. The two runs are then compared using the fingerprints of the findings they produced.

They do not need to use identical prose. They do need to identify essentially the same underlying issues.

Our current regression set is at 100% fingerprint consistency across the agent fleet.

In practice it catches the failures that matter: a persona that finds eight issues on one run and five on the next, or one that silently drops a category after a prompt edit. A wording change that quietly destabilises the fleet shows up immediately as a consistency drop.

That does not prove that every finding is correct. It does give us a stable baseline, a degree of determinism. When a prompt, model or tool changes, we can see whether it has quietly made the system less deterministic.

It is the closest equivalent I have found to a regression test for this kind of analyst.

The practical lesson: for non-deterministic agents, you need a stability signal, not just an accuracy one before you start tuning prompts.

6. AI reviewing AI is useful—but not always useful to the operator.

One of the seven personas is an Auditor.

It reviews findings from the other six analyst agents and looks for gaps, agreement and disagreement. For example, it might identify that two personas assessed the same shared service differently, or that a significant repository area was overlooked entirely.

This is genuinely useful to me as the platform owner.

It is much less useful to someone who has opened an application page to decide what action to take. From their perspective, a discussion about disagreement between two AI personas is mostly system-level noise.

We therefore treat these findings differently in the interface. They appear in a demoted tier and are folded away by default.

Readers of the first article will recognise this as the third reverse-lookup bucket — the inter-persona quality-assurance tier that sits alongside findings which directly affect an application and those that mention it as part of a wider pattern. It earns its place for me as the platform owner, but it should not be the first thing an operator sees when they open an application page to decide what to do.

The information remains available, but it does not compete with findings that directly affect the application or decision in front of the user.

The practical lesson: AI-about-AI output is often platform telemetry, not primary user content.

The model was not the difficult part

The common thread through all six lessons is that none of them required a more capable model.

The difficult work was in the layer between a capable model and the person expected to act on its output:

·      identity and lifecycle management;

·      severity calibration;

·      structured grounding;

·      correction loops;

·      regression testing;

·      information hierarchy.

Model capability can produce a compelling finding.

The surrounding system determines whether anyone trusts it, understands it and does something about it.

What broke first in your AI-output-to-human-action pipeline?

#AIEnablement #EnterpriseArchitecture #EnterpriseAI #AIEngineering #AgenticAI #AIGovernance #DataQuality #ArchitectureManagement #Waltz

What’s your favourite benchmark? The one you instinctively turn to when you want to get a feeling for the performance of a system.

Thera are, of course, no right or wrong answers here (except LINPACK 😀, kidding!) just whichever one most closely resembles your real workload.

I don’t think it’s much of a secret that we work primarily in financial services and the benchmark data we have chosen to include in FLOPx (our cloud and metal performance comparison tool) is quite FSI biased. We’re looking to expand it. Both with additional benchmarks within the finance world but also more broadly with other HPC/ supercomputing relevant workloads.

The obvious missing data point is the QuantLib benchmark (regardless of my own feelings about it) but what else would you like to see?

Would having the results from the SPEC benchmark suite be useful or is that data widely and easily available such that there’s no additional value?

Anything from the Phoronic HPC test suite? OpenFOAM perhaps?

I’m sure you’d love to have them all, but we have to pay to run these ourselves and I haven’t stuck a “dot AI” at the end of our name yet so I’m not made of money 🤣

Answers on a postcard to HMx Towers please.