Comparison
Pipeline Parallelism vs Pretraining
Pipeline Parallelism and Pretraining are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Pipeline Parallelism
Pipeline Parallelism comes up when the question is fundamentally about infrastructure.
A 405B model trained on 4096 GPUs: TP=8 within node × PP=16 across the pod × DP=32 across pods.
When you would reach for Pretraining
Pretraining comes up when the question is fundamentally about training.
GPT-3 pretrained on ~300B tokens.
Frequently asked
What is the difference between Pipeline Parallelism and Pretraining?
Pipeline Parallelism: Pipeline parallelism splits the model by layer across GPUs — GPU 1 holds layers 0-15, GPU 2 holds 16-31, etc. Forward passes flow through the pipeline like an assembly line. Pretraining: Pretraining is the initial training phase where an LLM learns to predict the next token on trillions of tokens of general text. It produces a base model that can be adapted later.
When should I use Pipeline Parallelism vs Pretraining?
Pipeline Parallelism is the right concept when you are focused on infrastructure. Pretraining applies when you are focused on training.
Are Pipeline Parallelism and Pretraining the same thing?
No. Pipeline Parallelism is infrastructure; Pretraining is training. They are related but address different parts of the AI stack.