Artificial Intelligence Workloads

Abstract

With the deep application of artificial intelligence (AI) technology in fields such as computer vision, natural language processing, deep learning training, and inference, AI workloads exhibit characteristics such as high computational intensity, large data throughput, and frequent memory access. Traditional GPU hardware configurations are no longer able to meet their efficient operation requirements. This article aims to improve the efficiency of AI workload operation and reduce resource consumption, focusing on optimizing GPU core hardware components. Firstly, analyze the typical characteristics of AI workloads, including computational parallelism, data locality, and memory access patterns; Subsequently, control experiments were designed to quantify the impact of GPU core frequency, VRAM bandwidth, CUDA core quantity, and multi GPU interconnect architecture on the performance of AI tasks (image classification, Transformer model inference); Finally, an adaptive optimization strategy for GPU configuration based on workload types is proposed, which achieves a balance between performance and energy consumption by dynamically adjusting hardware parameters. The experimental results show that in the ResNet-50 image classification task, the optimized GPU configuration can increase training speed by 23.5% and reduce energy consumption by 18.2%; In the BERT model inference task, latency decreased by 19.8% and throughput increased by 21.1%. This study provides theoretical basis and practical reference for GPU hardware selection and configuration optimization of AI servers.