Optimizing Large Language Model Scaling with Micro Batch Pipeline and Inference Parallelism

Abstract Natural language processing has seen transformative progress with the development of sophisticated models capable of generating and understanding human language with high accuracy. The novel concept of integrating micro batch pipeline and inference parallelism represents a significant leap in optimizing the scalability and efficiency of these models. Through comprehensive experimentation with a modified GPT-Neo, substantial improvements were achieved in throughput, latency, perplexity, and BLEU scores, highlighting the effectiveness of the proposed methodologies. The enhanced model demonstrated superior performance in processing large datasets, maintaining high accuracy and quality of outputs, thereby addressing critical bottlenecks in computational load and resource constraints. The study demonstrates the potential of advanced parallelism techniques in revolutionizing model training and deployment, contributing valuable insights into the future of natural language processing and artificial intelligence.