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The engineering samples of the NVIDIA Grace CPU Superchip and NVIDIA Grace Hopper Superchips were tested using different benchmarks and scientific applications. The benchmarks include HPCC and HPCG. The real application-based benchmark includes AI-Benchmark-Alpha (a TensorFlow benchmark), Gromacs, OpenFOAM, and ROMS. The performance was compared to multiple Intel, AMD, ARM CPUs and several x86 with NVIDIA GPU systems. A brief energy efficiency estimate was performed based on TDP values. We found that in HPCC benchmark tests, the per-core performance of Grace is similar to or faster than AMD Milan cores, and the high core count often allows NVIDIA Grace CPU Superchip to have per-node performance similar to Intel Sapphire Rapids with High Bandwidth Memory: slower in matrix multiplication (by 17%) and FFT (by 6%), faster in Linpack (by 9%)). In scientific applications, the NVIDIA Grace CPU Superchip performance is slower by 6% to 18% in Gromacs, faster by 7% in OpenFOAM, and right between HBM and DDR modes of Intel Sapphire Rapids in ROMS. The combined CPU-GPU performance in Gromacs is significantly faster (by 20% to 117% faster) than any tested x86-NVIDIA GPU system. Overall, the new NVIDIA Grace Hopper Superchip and NVIDIA Grace CPU Superchip Superchip are high-performance and most likely energy-efficient solutions for HPC centers.
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This content will become publicly available on February 19, 2026
The AmpereOne A192-32X in Perspective: Benchmarking a New Standard
This study presents a comprehensive benchmarking analysis of the Arm-based AmpereOne A192-32X CPU, a high-performance but low power processor designed for cloud-native workloads characterized by high core occupancy, imperfectly-vectorized or even pure scalar software, limited need for high floating-point performance, and, increasingly, AI inference. These traits also characterize much of academic research computing. Hence a thorough investigation of this novel CPU seeking to characterize its strengths and weaknesses on academic workloads, including traditional HPC codes for which it was not designed, will shed light on its relevance in a research setting. We report comparative analyses with contemporary CPUs (Intel Sapphire Rapids, AMD EPYC, NVIDIA Grace-Grace) and illustrate AmpereOne’s architectural advantages in handling parallel workloads and optimizing power consumption. The CPUs are compared in terms of performance and power consumption using a wide range of applications covering different workloads and disciplines.
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- PAR ID:
- 10626839
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400713422
- Page Range / eLocation ID:
- 23 to 35
- Format(s):
- Medium: X
- Location:
- Hsinchu Taiwan
- Sponsoring Org:
- National Science Foundation
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