Intel SC23 Update: 1-Trillion Parameter AI Model Running on Aurora Supercomputer, Granite Rapids Benchmarks

Intel Supercomputer
(Image credit: Intel)

At Supercomputing 2023, Intel provided a slew of updates on its latest HPC and AI initiatives, including new information about its fifth-gen Emerald Rapids and future Granite Rapids Xeon CPUs, Guadi accelerators, new Max Series GPU benchmarks against Nvidia's H100 GPUs, and the company's work on the 'genAI' 1-trillion-parameter AI model that runs on the Aurora supercomputer.

Upon completion, Aurora is widely expected to take the crown as the fastest supercomputer in the world at two Exaflop/s (EFlop/s) of performance. However, Intel hasn't yet shared details about Aurora's formal benchmark submission for the Top500 list — the company says it's leaving that announcement to the Department of Energy and the Argonne National Laboratory. If tradition holds, the Top500 organization will release those hotly-anticipated results later today. In the meantime, Intel's update includes plenty of new tidbits to chew over. 

Aurora Supercomputer Benchmarks

Paul Alcorn
Editor-in-Chief

Paul Alcorn is the Editor-in-Chief for Tom's Hardware US. He also writes news and reviews on CPUs, storage, and enterprise hardware.

  • weber462
    Support Petals AI. Support BOINC. Normal people should have access to AI. Normal people should have access to processing power.
    Reply
  • vanadiel007
    weber462 said:
    Support Petals AI. Support BOINC. Normal people should have access to AI. Normal people should have access to processing power.

    No. It's only a matter of time before we see AI coin that can only be mined using AI neural networks.
    Reply
  • bit_user
    new Max Series GPU benchmarks against Nvidia's H100 GPUs
    It's telling they only compared themselves to the H100 on non-AI workloads. For AI, they could only compete against the older A100.
    Reply
  • bit_user
    weber462 said:
    Support Petals AI. Support BOINC. Normal people should have access to AI. Normal people should have access to processing power.
    As far as I can tell, Petals is only usable for inference. For training (and many HPC workloads), you need all that processing power to be very tightly-integrated with high-bandwidth interconnects and having fast access to huge amounts of data.

    As far as normal people having access to it, there are cloud providers (including Nvidia) where you can rent time.
    Reply
  • jkflipflop98
    weber462 said:
    Support Petals AI. Support BOINC. Normal people should have access to AI. Normal people should have access to processing power.

    Normal people don't have the electrical capacity to even boot up the amount of silicon it takes to run something like this. You basically need your own electrical sub-station to deliver the amount of energy this kind of thing takes.

    Sure, in 20 years any old laptop at bestbuy will be able to do what these systems are doing, but for right now your best bet is to do as BU says and rent some cloud time.
    Reply
  • DougMcC
    I threw up a little bit when I read the page about the project written in Fortran.
    Reply
  • JayNor
    21,248 x 3
    is number of PVC GPUs
    2 CPUs and 6 GPUs per node.
    Reply
  • bit_user
    jkflipflop98 said:
    Normal people don't have the electrical capacity to even boot up the amount of silicon it takes to run something like this.
    I think you missed @weber462 's point, which was to suggest that distributed computing could substitute for these kinds of supercomputers - not that an ordinary person would have even a single one of the Aurora-class machine in their home.

    As I pointed out, distributed computing is only applicable to a rather limited set of problems. It's great when it does apply.

    jkflipflop98 said:
    Sure, in 20 years any old laptop at bestbuy will be able to do what these systems are doing, but for right now your best bet is to do as BU says and rent some cloud time.
    I'm not saying you're wrong, but I think there's probably not (yet) a technology roadmap that would get us there. More difficult than merely scaling compute performance is going to be improving energy efficiency to match.

    Anyone interested in the subject should take a close read through this (especially the slides):
    https://wccftech.com/amd-lays-the-path-to-zettascale-computing-talks-cpu-gpu-performance-plus-efficiency-trends-next-gen-chiplet-packaging-more/
    Reply
  • bit_user
    DougMcC said:
    I threw up a little bit when I read the page about the project written in Fortran.
    I don't see where it says that in the article, but I would point out that Fortran has continued evolving, like C, C++, and other mature languages:
    https://en.wikipedia.org/wiki/Fortran#Modern_Fortran
    I've never used Fortran, but I'd be surprised if they hadn't added enough quality-of-life improvements to it, for it to be something I could live with.
    Reply