Traditional CPU servers are difficult to meet the training requirements of complex deep learning models in terms of parallel processing capabilities, floating-point computing efficiency, and data throughput. Therefore, GPU servers have gradually become an indispensable core infrastructure for AI training. Compared with traditional computing platforms, GPU servers have shown significant advantages in training performance, energy efficiency, scalability, and ecological compatibility.
Graphics processing units (GPUs) were originally used for graphics rendering and 3D computing. Their core feature is that they have hundreds or thousands of stream processors, which are suitable for large-scale parallel computing. Deep learning training is essentially a large number of matrix multiplications, convolution operations, and vector operations. These tasks are highly compatible with the parallel computing capabilities of GPUs.
GPUs usually have thousands of CUDA cores (such as NVIDIA A100 has 6912 cores), while traditional CPUs generally have only dozens of general-purpose cores. The large amount of matrix calculations involved in the training of deep neural networks can be decomposed into thousands of small tasks by GPUs for concurrent execution, greatly reducing training time.
AI training requires frequent reading and writing of model parameters and intermediate results. GPU memory (such as HBM2e) provides hundreds of GB/s of bandwidth, which is unmatched by DDR4 memory. High-bandwidth memory can avoid memory bottlenecks and ensure that data channels do not become performance shortcomings.
Modern GPUs have mixed-precision computing capabilities, such as BF16 that supports FP32, FP16, and even lower precision. Using mixed-precision training can significantly speed up training and reduce memory usage without sacrificing model accuracy.
NVIDIA's Tensor Core is designed for AI computing and can provide extremely high Tensor throughput at FP16 precision. Compared with CPUs and early GPUs, modern GPUs have doubled their training performance. It can significantly accelerate the training process, reduce memory pressure, and train larger models; improve energy utilization and server density. FP16 computing is not only suitable for image tasks, but also performs well in a wide range of model structures such as Transformer architecture, GAN, and RNN.
The AI training process is accompanied by the reading of massive sample data. Especially in image, video, and voice tasks, the input and output pressure is much greater than the traditional server load. GPU servers combined with high-speed SSDs or distributed file systems (such as Ceph and NFS) can effectively support high-speed IO requirements.
Graphics card servers not only have advantages at the hardware level, but the software ecosystem behind them is also the key to training acceleration. Taking NVIDIA as an example, it provides a complete AI development stack that allows users to easily deploy AI training environments on GPU servers, achieving one-click construction, multi-environment parallelism, and rapid model iteration. The open source community has strong support for GPU architecture, frequent daily updates, and considerable guarantees for driver compatibility and performance tuning.
The advantages of GPU servers in the field of AI training have become an industry consensus. With powerful parallel computing capabilities, high-speed video memory, high-bandwidth IO, complete software stack support, and flexible deployment methods, GPU servers have become an irreplaceable core infrastructure for deep learning and machine learning.
