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Comment by xadhominemx | Hacker News Reader

Agreed, the CPUs are not performing the scientific calculations in this system.

Also note — this project is quite modest in scale. Dozens of GenAI clusters larger than this computer will be installed at cloud data centers in the next 18 months.

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"The Jupiter will instead have SiPearl’s ARM processor based on ARM’s Neoverse V1 CPU design. SiPearl has designed the Rhea chip to be universally compliant with many accelerators, and it supports high-bandwidth memory and DDR5 memory channels."

And

"Jülich is also building out its machine-learning and quantum computing infrastructure, which the supercomputing center hopes to plug in as accelerator modules hosted at its facility."

So a modular setup, where different aspects can be upgraded as needed. Btw:

> Also note — this project is quite modest in scale.

"Exascale" and €273M doesn't sound modest to me. No matter what it's compared against.

At €300m, the Jupiter will be ~10,000 H100s. Each major CSP will have several clusters this size or larger within a few quarters.

Almost all of the AI computers being built now are relatively modestly sized compared to a supercomputer. All but the biggest ones are at or under the low hundreds of nodes (low thousands of GPUs). The only real exceptions are the few AI hyperscale companies that want to sell GPU computing to others.

Do AI hyperscalers devote a their whole system to one big run anyway?

If they don’t, then those are big clusters in the sense that AWS is the world’s biggest supercomputer, which is to say, not.

AWS is not a supercomputer because it doesn't have high-adjacency networking. If AWS turned its biggest region loose on Linpack, I would be surprised if they cracked the top 50 on the supercomputer list, despite probably having more cores than #1.

The AI hyperscalers certainly claim to be able to devote 100% of cluster capacity to one training run. Google is training some huge models, OpenAI is also.

I don't know how you can describe "equal to the world's fastest supercomputer, which was built less than a year ago" as "quite modest".

Will these GenAI clusters have similar interconnects and ability to run scientific computing/HPC codes? AI has moved over to ASICS and GPUs nowadays, right? I also have no idea what their interconnect requirements are, but the task seems pretty low communication, I wonder if they can get by with a cheaper interconnect.

AI training requires a lot of global reductions which must be very fast otherwise everything slows down. So they also require fast and low latency interconnects.

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