• Proposes a novel algorithm for automatic parallelism strategy generation with minimal memory redundancy.
• Introduces a redundant memory cost model to compute memory overhead per operator in distributed training.
• Formulates the parallelism strategy search as an integer linear programming (ILP) problem for optimal intra-operator parallelism.
• Achieves up to 67% memory savings versus Megatron-LM while maintaining comparable throughput.