📄 中文摘要
人工智能模型的增长速度超出了传统计算环境的支持能力，训练工作负载变得愈加复杂和资源密集。GPU在人工智能训练中已成为一种实用的必要工具，而非单纯的技术升级。AI训练涉及对大规模数据集进行重复的数学运算，GPU能够并行处理这些运算，从而在深度学习任务中超越CPU。其并行架构使得在训练过程中，尤其是当模型包含数百万或数十亿个参数时，能够更快地收敛。
用于人工智能训练的GPU：在不超越基础设施限制的情况下扩展模型
📄 English Summary
GPU for AI Training: Scaling Models Without Hitting Infrastructure Limits
The rapid growth of artificial intelligence models has outpaced the capabilities of traditional computing environments, leading to increasingly complex and resource-intensive training workloads. GPUs have become a practical necessity for AI training rather than merely a technical upgrade. AI training involves repetitive mathematical operations applied to massive datasets, and GPUs are designed to handle these operations concurrently, allowing them to outperform CPUs in deep learning tasks. Their parallel architecture enables faster convergence during training, particularly when models contain millions or billions of parameters.