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Tensor Parallelism
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Overview
Use caseparallelizing tensor operations across multiple devices or machines
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Last updatedUpdated 5 days ago
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Tensor Parallelism

concept

Splitting individual tensor operations across multiple devices for parallel computation within single layers.

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primary use case

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parallelizing tensor operations across multiple devices or machines○UnverifiedHighFresh1
distributing neural network computation across multiple GPUs by splitting tensors○UnverifiedHighFresh1

requires

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multiple GPUs or compute devices○UnverifiedHighFresh1
high-bandwidth interconnect between GPUs○UnverifiedHighFresh1

splits

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weight matrices across multiple devices○UnverifiedHighFresh1

used in

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distributed deep learning training○UnverifiedHighFresh1

enables

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training models larger than single GPU memory○UnverifiedHighFresh1
training and inference of large language models that exceed single GPU memory○UnverifiedHighFresh1

used by

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Megatron-LM○UnverifiedHighFresh1
FairScale○UnverifiedModerateFresh1

supports model

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Transformer models○UnverifiedHighFresh1
large language models○UnverifiedHighFresh1
transformer neural networks○UnverifiedHighFresh1

reduces

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memory requirements per GPU○UnverifiedHighFresh1

integrates with

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TensorFlow○UnverifiedHighFresh1
Megatron-LM○UnverifiedHighFresh1
PyTorch○UnverifiedModerateFresh1
DeepSpeed○UnverifiedModerateFresh1

based on

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matrix multiplication decomposition○UnverifiedModerateFresh1
matrix partitioning techniques○UnverifiedModerateFresh1

alternative to

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Data Parallelism○UnverifiedModerateFresh1
pipeline parallelism○UnverifiedModerateFresh1

Alternatives & Similar Tools

Data Parallelism
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pipeline parallelism
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Commonly Used With

TensorFlowMegatron-LMPyTorchDeepSpeed

Related entities

Claim count: 22Last updated: 4/5/2026Edit history