Parallelism
Tensor parallelism splits work within model layers and exchanges partial results during those layers. Pipeline parallelism places whole layer ranges on stages and communicates activations at stage boundaries.
Tensor sharding favors fast repeated collectives, while pipeline staging depends on balanced work and enough microbatches to limit bubbles. Model size, topology, batch shape, and latency targets decide the fit.
Teams compare feasible layouts using the same model, devices, and request distribution. Memory headroom, collective time, stage imbalance, and end-to-end latency remain visible.
Tensor parallelism splits work within model layers and exchanges partial results during those layers. Pipeline parallelism places whole layer ranges on stages and communicates activations at stage boundaries. Teams compare feasible layouts using the same model, devices, and request distribution. Memory headroom, collective time, stage imbalance, and end-to-end latency remain visible.
Tensor sharding favors fast repeated collectives, while pipeline staging depends on balanced work and enough microbatches to limit bubbles. Model size, topology, batch shape, and latency targets decide the fit.
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