---
title: "AI parallelism training strategies: Data, model, tensor and pipeline parallelism"
url: https://daily.dev/posts/ai-parallelism-training-strategies-data-model-tensor-and-pipeline-parallelism-yeav4ssfg
source_url: https://www.datasciencecentral.com/ai-parallelism-training-strategies-data-model-tensor-and-pipeline-parallelism/
type: article
source: "Data Science Central"
published: 2025-12-02T16:50:19.907Z
updated: 2025-12-02T16:50:40.720Z
tags: ["machine-learning", "deep-learning", "gpu", "distributed-systems"]
reading_time: 7
upvotes: 0
comments: 0
language: en
---

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# AI parallelism training strategies: Data, model, tensor and pipeline parallelism

**[Data Science Central](https://daily.dev/sources/ds_central)** · 7 min read · 0 upvotes · 0 comments

## Summary

Training large AI models requires distributing work across multiple GPUs due to memory and compute constraints. Five parallelism strategies address these challenges: data parallelism splits batches across devices, model parallelism divides layers across GPUs, tensor parallelism partitions weight matrices, pipeline parallelism creates sequential stages with micro-batches, and hybrid parallelism combines all approaches for maximum scale. Supporting techniques like ZeRO optimizer sharding, activation checkpointing, and offloading further reduce memory pressure. Strategy selection depends on model size—data parallelism for small models, tensor/model parallelism for large models, and hybrid approaches for massive models on clusters.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.datasciencecentral.com/ai-parallelism-training-strategies-data-model-tensor-and-pipeline-parallelism/>

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Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#deep-learning](https://daily.dev/tags/deep-learning), [#gpu](https://daily.dev/tags/gpu), [#distributed-systems](https://daily.dev/tags/distributed-systems)

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