NVIDIA EOS: A Showcased Top 10 Supercomputer 

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Recently, NVIDIA revealed more details about its impressive Top 10 supercomputer, the NVIDIA EOS. Currently holding its place at position #9 on the November 2023 Top500 list, the EOS displays formidable power at the double precision linpack benchmark. This is particularly notable since NVIDIA has been focusing less on double precision performance in favor of AI capabilities in recent generations. Nevertheless, the EOS’s high ranking is impressive, especially since it wasn’t primarily designed for this measure. Consequently, it represents a superb AI supercomputer.

NVIDIA EOS: An Exemplary Top 10 Supercomputer

The NVIDIA EOS is a powerhouse with 576 NVIDIA DGX H100 systems, operating with the NVIDIA Quantum-2 400Gb/s Infiniband for an Rmax of 121.4 PFlops/s on double precision linpack, alongside 18.4 exaflops of FP8 AI compute power.

Given There are 576 DGX H100 systems present in the EOS which equates to 4608 GPUs. Realistically, the cost of assembling such a machine at market prices would likely exceed a staggering $200M+.

NVIDIA is leveraging its SuperPOD architecture to construct EOS in expandable units, a feature that allows for scaling to larger topologies. A significant aspect of this announcement is the highlight of NVIDIA’s ability to scale up to 4608 accelerators, a feat that many other AI training cards struggle to match.

As visible, NVIDIA uses four systems per rack, consuming no more than 32kW per rack. This estimate stands valid until the back of the systems comes into view.

In the above snapshot, behind the individual on the KVM cart, the liquid cooling rack manifolds are visible.

Because of the transition from air to liquid’s power efficiency gains, liquid cooling will be desired for future AI servers. If deploying AI servers is in your plans, liquid cooling should be on your mind, as indicated by NVIDIA with EOS.

In Conclusion

NVIDIA’s EOS supercomputer is not only utilized for the company’s internal development but also serves its customers. Possessing a large-scale cluster sets NVIDIA apart from several of its competitors.

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Samantha Rattner
Samantha Rattner

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