AI model parallelism is used when a model or required throughput cannot be served efficiently on one accelerator. Tensor, pipeline and other strategies divide different parts of the model computation across devices.
Parallelism increases available memory and compute but introduces communication overhead. Performance therefore depends on partitioning, batch shape and the speed and topology of the network connecting accelerators.
ELI5
Model parallelism splits one AI model across several processors so they cooperate on the same workload. It is useful when the model is too large for one device or needs more compute than one device can provide.
For example, two accelerators can each hold different model layers and pass intermediate results between them. This adds memory and compute, but slow communication between the devices can cancel some of the speed benefit.
How is model parallelism different from data parallelism?
Model parallelism divides one model across devices, while data parallelism places model replicas on devices that process different requests or examples.
Why does model parallelism need fast networking?
Devices must exchange intermediate activations or partial results during computation, and slow transfers can erase the benefit of additional processors.
