TR 7000 vs. Intel: AI and Inferencing

As technology progresses at a breakneck pace, so too do the demands of modern applications and workloads. With artificial intelligence (AI) and machine learning (ML) becoming increasingly intertwined with our daily computational tasks, it's paramount that our reviews evolve in tandem. Recognizing this, we have AI and inferencing benchmarks in our CPU test suite for 2024. 

Traditionally, CPU benchmarks have focused on various tasks, from arithmetic calculations to multimedia processing. However, with AI algorithms now driving features within some applications, from voice recognition to real-time data analysis, it's crucial to understand how modern processors handle these specific workloads. This is where our newly incorporated benchmarks come into play.

As chip makers such as AMD with Ryzen AI and Intel with their Meteor Lake mobile platform feature AI-driven hardware within the silicon, it seems in 2024, and we're going to see many applications using AI-based technologies coming to market.

We are using DDR5-5200 RDIMM memory on the Ryzen Threadripper 7980X and 7970X as per JEDEC specifications. For Intel's Xeon W9-3495X, we are using DDR5-4800 RDIMM memory as per Intel's JEDEC specifications. It should be noted that both platforms are run with their full allocation of memory channels, eg, TR7000 in 4-channel and Sapphire Rapids in 8-channel.

Below are the settings we have used for each platform:

  • DDR5-5200 RDIMM - AMD Threadripper 7000
  • DDR5-4800 RDIMM - Intel Xeon Sapphire Rapids WS
  • DDR5-5600B CL46 - Intel 14th Gen
  • DDR5-5200 CL44 - Ryzen 7000

(6-1) ONNX Runtime 1.14: CaffeNet 12-int8 (CPU Only)

(6-1b) ONNX Runtime 1.14: CaffeNet 12-int8 (CPU Only)

(6-1c) ONNX Runtime 1.14: Super-Res-10 (CPU Only)

(6-1d) ONNX Runtime 1.14: Super-Res-10 (CPU Only)

(6-2) DeepSpeech 0.6: Acceleration CPU

(6-3) TensorFlow 2.12: VGG-16, Batch Size 16 (CPU)

(6-3b) TensorFlow 2.12: VGG-16, Batch Size 64 (CPU)

(6-3d) TensorFlow 2.12: GoogLeNet, Batch Size 16 (CPU)

(6-3e) TensorFlow 2.12: GoogLeNet, Batch Size 64 (CPU)

(6-3f) TensorFlow 2.12: GoogLeNet, Batch Size 256 (CPU)

Although there are various AI and machine learning algorithms and models, different architectures and characteristics play a different part within the ecosystem. Looking at performance in the ONNX Runtime benchmark using CaffeNet 12-int8, we see much better performance from faster cores, and in the case of the Ryzen 9 7950X3D, the additional 3D V-Cache plays a massive part in performance here. Using the Super-Res-10 model, the Intel Xeon W9-3495X is much more optimized here than the rest of the chips, with the Threadripper 7000 pairing performing closely with the desktop CPUs tested.

In the DeepSpeech benchmark, the desktop CPUs reign supreme, whereas in TensorFlow with VGG, the Xeon W9-3495X has a good advantage over the Threadripper 7000 chips. As the batch size increased, the Threadripper 7980X and 7970X performed better than the desktop chips, such as with a 64 and larger 256 batch size.

TR 7000 vs. Intel: Science And Simulation Conclusion
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  • Jansen - Monday, November 20, 2023 - link

    Bit disappointing that the memory controller only supports DDR5-5200, considering that JEDEC compliant DDR5-6400 RDIMMs are available.
  • Ryan Smith - Monday, November 20, 2023 - link

    At the end of the day it's the same I/O die as Genoa. So it comes with roughly the same restrictions.
  • TEAMSWITCHER - Monday, November 20, 2023 - link

    Not surprised by this at all. My 3960X Threadripper system was never able to run 64gb (16gb x 4) at even the promised DDR4-3200 speed. I tried three different RAM kits and even a different CPU (replaced by AMD) and the problem never went away. In the end I believe it to be motherboard issue, it was simply incapable of running stable with any RAM faster than DDR-3000.

    After spending so much on the ASUS Zenith II Extreme Alpha motherboard, 64GB of DDR-3600 RAM, and a $1400 CPU, the end result was very disappointing. Support from ASUS, AMD, and G-Skill was a long process, and eventually I had to just accept what was working and move on.

    Ultimately, I don't believe that AMD and ASUS can properly deliver and support any HEDT platform that is worth the money they ask for it. I sincerely wish Intel would return to this segment, as I never had a problem with my X99 Deluxe II motherboard.
  • lemans24 - Monday, November 20, 2023 - link

    Intel is definitely in HEDT with their xeon w-2400 chips
  • StormyParis - Monday, November 20, 2023 - link

    I've blacklisted Asus. Lots of issues with both specs, reliability, and service.
  • vfridman - Monday, November 20, 2023 - link

    I have two systems with 3990X and two systems with 3970X, ASUS Zenith II Extreme Alpha motherboard and 256GB of 3600 speed G.Skill RAM in each system. All runs perfectly and completely stable, even with maxed out PBO overclock. I regularly run compilation jobs that require almost entire 256GB of RAM and never experienced any problems. I suspect you got unlucky with your CPU memory controller.
  • Mikewind Dale - Tuesday, November 21, 2023 - link

    I have a ThreadRipper Pro 3950X on a Supermicro WRX80 motherboard. I run 8x64 (512) GB of Supermicro-branded DDR4 3200 ECC RDIMM without a problem.
  • Adam7288 - Wednesday, November 22, 2023 - link

    Same exact config! Ram Bros.
  • tygrus - Saturday, January 6, 2024 - link

    How are you going with those >200GB matrices & statistics?
    Many years ago I had to use raw frequency stats, then a program to generate blocks of SAS code that could analyse cross-tab by cluster (weighted) with smaller subsets of interest from every possible combination (multi-morbidity data). Making sure the stats methods still gave correct results. Divide & conquer to fit in limited RAM of circa 2013 computers. In those days it was mostly constrained by single thread & disk/network IO speed (~100MB/s).
  • TEAMSWITCHER - Friday, November 24, 2023 - link

    Ya know.. I have yet to build an AMD system that didn't suffer from some kind of issue. I don't think I'm unlucky either. I need to stop buying AMD gear thinking... "this time will be different." Because it never is.

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