Tensor parallelism partitions operations within a layer, while pipeline parallelism assigns different layer groups to stages. Communication and scheduling must be balanced so devices do not spend excessive time waiting.
Model parallelism splits a model's computation or parameters across multiple devices because one device is insufficient or slower.
Tensor parallelism partitions operations within a layer, while pipeline parallelism assigns different layer groups to stages. Communication and scheduling must be balanced so devices do not spend excessive time waiting.