Comparison
GPU vs Pipeline Parallelism
GPU and Pipeline Parallelism are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for GPU
GPU comes up when the question is fundamentally about infrastructure.
NVIDIA H100: ~2 TB/s memory bandwidth, ~989 TF/s BF16.
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.
Frequently asked
What is the difference between GPU and Pipeline Parallelism?
GPU: GPUs are the parallel processors that train and run nearly every modern AI model. Their throughput on matrix multiplication is what makes deep learning practical. 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.
When should I use GPU vs Pipeline Parallelism?
GPU is the right concept when you are focused on infrastructure. Pipeline Parallelism applies when you are focused on infrastructure.
Are GPU and Pipeline Parallelism the same thing?
No. GPU is infrastructure; Pipeline Parallelism is infrastructure. They are related but address different parts of the AI stack.