DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72
Large language model (LLM) inference increasingly depends on multi-GPU execution, yet existing inference parallelization strategies require layer-wise inter-rank synchronization, making end-to-end performance sensitive to workload imbalance. We present DWDP (Distributed Weight Data Parallelism), an inference parallelization strategy that preserves data-parallel execution while offloading MoE weights across peer GPUs and fetching missing experts on demand. By removing collective inter-rank synchronization, DWDP allows each GPU to progress independently. We further address the practical overheads of this design with two optimizations for split-weight management and asynchronous remote-weigh...
| Authors | Wanqian Li, Jintao Peng, Zongfei Jing et al. |
|---|---|
| Primary category | cs.DC |
| Published | 2 April 2026 |
| Last updated at source | 12 May 2026 |
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