Heterogeneous parallelism decouples module layouts in multimodal LLM training via boundary communicators, yielding up to 49.3% TFLOPS/GPU gains in colocated mode and 13% throughput in non-colocated mode with convergence parity.
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Heterogeneous Parallelism for Multimodal Large Language Model Training
Heterogeneous parallelism decouples module layouts in multimodal LLM training via boundary communicators, yielding up to 49.3% TFLOPS/GPU gains in colocated mode and 13% throughput in non-colocated mode with convergence parity.