Efficient neural language model training system design based on machine learning

To address the problem of low computational resource scheduling efficiency in large-scale deep learning training, a distributed training system is designed with a three-layer architecture to achieve high-efficiency computational resource scheduling. The system introduces hybrid precision training and dynamic batch processing optimization strategies, achieving a training throughput of 14,000 samples per second on an 8-GPU cluster. Experimental tests show that the system performs exceptionally well in terms of training efficiency and resource utilization, with a GPU utilization rate of 94.5% and an inference latency of 32ms, providing a practical reference for large-scale model training deployment.