Comparison
GPU vs Training Compute
GPU and Training Compute 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 Training Compute
Training Compute comes up when the question is fundamentally about training.
GPT-3: ~3 × 10^23 FLOPs.
Frequently asked
What is the difference between GPU and Training Compute?
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. Training Compute: Training compute is the total floating-point operations used to pretrain a model, usually expressed as FLOPs (e.g. 10^25 FLOPs). It is the headline number governments now regulate.
When should I use GPU vs Training Compute?
GPU is the right concept when you are focused on infrastructure. Training Compute applies when you are focused on training.
Are GPU and Training Compute the same thing?
No. GPU is infrastructure; Training Compute is training. They are related but address different parts of the AI stack.