This article analyzes the performance differences between different GPU platforms in large language model training and inference. The study found that in the three stages of pre-training, fine-tuning and inference, the A800 GPU platform demonstrated significant performance advantages, with a throughput that was almost twice that of other consumer-grade GPUs, revealing the performance of consumer-grade GPUs in processing large model tasks. limitations. The article provides an in-depth comparison of three GPUs, RTX 3090, RTX 4090 and A800, and provides detailed runtime analysis, providing a valuable reference for optimizing the training and inference of large language models.
In pre-training, fine-tuning, and inference of large language models, the A800 GPU platform performs significantly better, with throughput almost doubling, revealing the limitations of consumer-grade GPUs in the field of large models. The study provides detailed runtime analysis of optimization techniques through an in-depth comparison of the performance of the RTX 3090, 4090, and A800.
All in all, the research results provide important guidance for selecting an appropriate GPU platform for large model training and inference, and also highlight the key role of high-performance computing platforms in promoting the development of AI technology. In the future, GPU optimization technology for large models will continue to develop to meet the growing computing needs.