Model parallelism is a distributed computing strategy where different parts of a machine learning model are processed simultaneously across multiple computing resources. This approach allows for the efficient training of large models that might not fit into the memory of a single machine, leveraging parallel processing to speed up computation and improve performance. It becomes especially important in scenarios where models require significant computational power and memory, ensuring that each component can be optimized independently while working together to produce predictions.
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