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Unveiling the Power of the Lenovo ThinkSystem SR675 V3 Server Through MLPerf Benchmarking


1 May 2024
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In the rapidly evolving realm of artificial intelligence and machine learning, the Lenovo ThinkSystem SR675 V3 server has proven its mettle by undergoing comprehensive testing using the MLPerf benchmark suite. This article explores the technical aspects and industry applications of the SR675 V3 server's performance based on the MLPerf results.


MLPerf benchmarks are the gold standard for evaluating the performance of machine learning models across diverse tasks. The Lenovo ThinkSystem SR675 V3 server, equipped with cutting-edge hardware, has been put through rigorous testing across various ML workloads, showcasing exceptional results that highlight its efficiency and capabilities.

Lenovo ThinkSystem SR675 V3
Figure 1. Lenovo ThinkSystem SR675 V3 configured with 8x 80GB PCIe H100 GPUs

Technical breakdown of MLPerf results

The results across different workloads on the SR675 V3 server and its configuration with 4x 96GB SXM5 H100 (AMD EPYC 9654 96-Core) and 8x 80GB PCIe H100 (AMD EPYC 9554 64-Core) to certify their performance.

Table 1. Benchmark results comparison (green indicates a higher result)
Server SR675 V3 SR675 V3
GPU 4x 96GB SXM5 H100 GPUs 8x 80GB PCIe H100 GPUs
Processor AMD EPYC 96C 9654 AMD EPYC 64C 9554
BERT 13.195 18.513
Mask RCNN 50.513 38.422
ResNet50 35.063 22.659
SSD 90.078 58.579
RNNT 31.503 26.398
3D-Unet 24.064 19.386

These results showcase the significant performance gains observed when utilizing 4x 80GB SXM5 H100 GPUs and the AMD EPYC 9654 96-Core processors, emphasizing the server's scalability and enhanced processing capabilities across various tasks.

Industry applications of SR675 V3's performance

The improved performance across these workloads has a myriad of real-world applications in diverse industries:

  • NLP in Customer Service and Financial Sectors

    Faster BERT inference speeds enable real-time sentiment analysis and document summarization for enhanced customer experiences and streamlined financial analyses.

  • Computer Vision in Retail and Manufacturing

    Quicker Mask RCNN and ResNet50 inference times facilitate faster object detection, aiding inventory management, defect detection, and quality control in manufacturing and retail.

  • Autonomous Vehicles and Surveillance

    SSD workload enhancements support faster object detection, crucial for real-time decision-making in traffic control and surveillance applications.

  • Speech Recognition in Healthcare and Telecommunications

    Improved RNNT performance can revolutionize speech recognition applications, benefiting healthcare transcription and voice-activated systems in telecommunications.

  • Healthcare Imaging and Research

    Speedier 3D-UNet inference can advance medical imaging for disease diagnosis, drug discovery, and treatment planning, potentially saving lives through timely diagnoses.


The MLPerf results for the SR675 V3 server and its performance with 80GB SXM5 H100 (AMD EPYC 9654 96-Core) unveil not only its technical capabilities but also its potential to drive innovation and efficiency across multiple industries. The SR675 V3 stands as a beacon of high-performance computing solutions, promising to power transformative applications and services in AI and machine learning.

From healthcare to retail, finance to autonomous systems, the SR675 V3 server's exceptional performance across various ML workloads underscores its significance in shaping the future of AI-driven applications. As organizations seek advanced technologies, the SR675 V3 server proves to be a leading solution in driving this technological evolution.

For more information

For more information, see the following resources:

MLCommons®, the open engineering consortium and leading force behind MLPerf, has now released new results for MLPerf benchmark suites:


Carlos Huescas is the Worldwide Product Manager for NVIDIA software at Lenovo. He specializes in High Performance Computing and AI solutions. He has more than 15 years of experience as an IT architect and in product management positions across several high-tech companies.

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