Unverified50% confidenceFactExact time
Distributed training refers to splitting a training task across multiple GPUs and machines for parallel execution when a single GPU cannot hold the model or data, with common strategies including data parallelism, tensor parallelism, and pipeline parallelism.
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10/21/2026
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UnverifiedDistributed training is mainly divided into data parallelism (such as PyTorch DDP) and model parallelism (such as Megatron-LM's pipeline parallelism).75% similarUnverifiedMoE models in distributed training typically require the simultaneous deployment of four strategies: data parallelism, tensor parallelism, pipeline parallelism, and expert parallelism.75% similarUnverifiedDistributed training strategies such as DeepSpeed ZeRO, FSDP, tensor parallelism, and pipeline parallelism are necessary tools for training large video models.74% similarUnverifiedTraining refers to the process of adjusting model parameters with massive amounts of data, requiring high-speed multi-GPU interconnection and long periods of stable operation, with extremely high demands on per-GPU compute and memory bandwidth.74% similarUnverified前沿模型训练需要极高的GPU间互联带宽，要求训练集群物理上集中在同一地点，不能简单分散到多个数据中心72% similar
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