Unverified50% confidenceFactExact time
Pipeline Parallelism vertically shards the model by layers, with different GPUs responsible for different layer groups; it is suited for cross-node deployment but requires micro-batch scheduling to reduce the idle waiting caused by pipeline bubbles.
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50%
Confidence
Long-term
Relevance
9/21/2026
First Seen
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Unverified流水线并行按模型的不同层将计算分配到不同设备，通信量仅为相邻层的激活张量，但存在流水线气泡问题73% similarUnverifiedThis test most likely deploys multiple GPUs using pipeline parallelism, with the first half of the model's layers on the first card and the second half on the second card.73% similarUnverifiedTensor Parallelism (TP) splits individual Transformer layer weight matrices across different GPUs, requiring All-Reduce communication per layer forward pass; Pipeline Parallelism (PP) splits the model by layers with lower communication volume but has bubbles that reduce utilization72% similarUnverifiedModel parallelism consists of two approaches: tensor parallelism, which splits tensors within a single layer across multiple cards, and pipeline parallelism, which assigns different layers to different devices.71% similar
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