Pipeline Parallelism
Definition
Pipeline parallelism distributes consecutive processing steps across multiple devices.
A parallelization strategy that distributes model layers or processing steps across multiple devices and passes micro-batches through the stages.
Simply explained
Like an assembly line, different stations simultaneously work on different small sub-packages.
Why it matters in practice
The term contributes to secure, maintainable, and high-performance web applications as well as resilient technical decisions.
Example from business
A model is distributed across four layer groups on GPUs and utilized with micro-batches.
Limits and pitfalls
- Waiting phases between pipeline sections, data transfer, and stored intermediate results can limit the expected speed or memory advantage.
Origin & history
GPipe and subsequent systems popularized pipeline parallelism for large networks.
Use in mid-sized companies
- Possible application: A model is distributed across four layer groups on GPUs and utilized with micro-batches.
Frequently asked questions
What does pipeline parallelism mean?
A parallelization strategy that distributes model layers or processing steps across multiple devices and passes micro-batches through the stages. What mistakes should be avoided? Ignoring pipeline bubbles, communication costs, and activation storage.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: