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Model Parallelism
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Overview
Use casedistributing neural network model parameters across multiple compute devices
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Model Parallelism

concept

Distributing model layers across multiple devices to handle models larger than single device memory capacity.

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requires

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multiple computational devices○UnverifiedHighFresh1
multiple GPU devices or compute nodes○UnverifiedHighFresh1
multiple GPU or TPU devices○UnverifiedHighFresh1
Multiple GPU devices or distributed computing infrastructure○UnverifiedHighFresh1
inter-device communication mechanisms○UnverifiedHighFresh1
inter-device communication protocols○UnverifiedHighFresh1
multiple GPUs or compute devices○UnverifiedModerateFresh1

category

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parallel computing technique○UnverifiedHighFresh1
distributed computing technique○UnverifiedHighFresh1

primary use case

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distributing neural network model parameters across multiple compute devices○UnverifiedHighFresh1
Distributing neural network model parameters across multiple devices or machines to handle models too large for single device memory○UnverifiedHighFresh1
distributing neural network model parameters across multiple devices or machines○UnverifiedHighFresh1
distributing neural network layers across multiple devices or machines○UnverifiedHighFresh1
distributing neural network model layers across multiple devices or machines○UnverifiedHighFresh1
distributing neural network model parameters across multiple devices or processors○UnverifiedHighFresh1
distributed training of large neural networks across multiple devices○UnverifiedHighFresh1
training models that exceed single device memory capacity○UnverifiedHighFresh1
training large language models that exceed single device memory○UnverifiedHighFresh1
reducing memory requirements per device during training○UnverifiedHighFresh1

supported by

ValueTrustConfidenceFreshnessSources
PyTorch○UnverifiedHighFresh1
TensorFlow○UnverifiedHighFresh1
JAX○UnverifiedHighFresh1
Horovod○UnverifiedModerateFresh1

addresses problem

ValueTrustConfidenceFreshnessSources
memory limitations when training large neural networks○UnverifiedHighFresh1

technique type

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distributed computing optimization○UnverifiedHighFresh1

enables technique

ValueTrustConfidenceFreshnessSources
training large neural networks that exceed single device memory○UnverifiedHighFresh1
splitting neural network layers across multiple GPUs or devices○UnverifiedHighFresh1

enables

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training large neural networks that exceed single device memory○UnverifiedHighFresh1
training and inference of large neural networks that exceed single device memory○UnverifiedHighFresh1

technique category

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distributed deep learning○UnverifiedHighFresh1

splits

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neural network layers across devices○UnverifiedHighFresh1

addresses limitation

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single device memory constraints○UnverifiedHighFresh1

essential for

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training transformer models with billions of parameters○UnverifiedHighFresh1

use case

ValueTrustConfidenceFreshnessSources
training large language models○UnverifiedHighFresh1

implemented in

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

supports model

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transformer models○UnverifiedHighFresh1
large language models○UnverifiedHighFresh1
transformer architectures○UnverifiedHighFresh1
large transformer models○UnverifiedModerateFresh1

alternative to

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data parallelism○UnverifiedHighFresh1

implemented in framework

ValueTrustConfidenceFreshnessSources
PyTorch○UnverifiedHighFresh1
TensorFlow○UnverifiedHighFresh1
JAX○UnverifiedModerateFresh1

used with

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GPU clusters○UnverifiedHighFresh1
TPU pods○UnverifiedModerateFresh1

based on

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distributed computing principles○UnverifiedHighFresh1

used for

ValueTrustConfidenceFreshnessSources
large language model training○UnverifiedHighFresh1
training large language models○UnverifiedHighFresh1

integrates with

ValueTrustConfidenceFreshnessSources
PyTorch○UnverifiedHighFresh1
TensorFlow○UnverifiedHighFresh1
DeepSpeed○UnverifiedModerateFresh1
Horovod○UnverifiedModerateFresh1
Megatron-LM○UnverifiedModerateFresh1
FairScale○UnverifiedModerateFresh1

supports model type

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transformer models○UnverifiedHighFresh1

enables scaling

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models with billions to trillions of parameters○UnverifiedHighFresh1

used by system

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GPT-3○UnverifiedModerateFresh1
PaLM○UnverifiedModerateFresh1

commonly used with

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pipeline parallelism○UnverifiedModerateFresh1

combines with

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pipeline parallelism○UnverifiedModerateFresh1

complementary technique

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Pipeline Parallelism○UnverifiedModerateFresh1

challenge

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communication overhead between devices○UnverifiedModerateFresh1
device underutilization during sequential execution○UnverifiedModerateFresh1

implementation approach

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tensor parallelism across attention heads and feed-forward layers○UnverifiedModerateFresh1

supports protocol

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NCCL○UnverifiedModerateFresh1
MPI○UnverifiedModerateFresh1
gradient synchronization protocols○UnverifiedModerateFresh1

complementary to

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Pipeline Parallelism○UnverifiedModerateFresh1
data parallelism○UnverifiedModerateFresh1

competes with

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pipeline parallelism○UnverifiedModerateFresh1

communication pattern

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sequential forward and backward passes between devices○UnverifiedModerateFresh1

Alternatives & Similar Tools

data parallelism
alternative to
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pipeline parallelism
competes with
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Commonly Used With

PyTorchTensorFlowDeepSpeedHorovodMegatron-LMFairScale

Related entities

Claim count: 75Last updated: 4/5/2026Edit history