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SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
In this paper, we propose a scalable evaluation methodology (SAIH) for analyzing the AI performance trend of HPC systems with scaling the problem sizes of customized AI applications.
EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models
Although the AI community has expanded the model scale to the trillion parameter level, the practical deployment of 10-100 billion parameter models is still uncertain due to the latency, throughput, and memory constraints.
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