Comparison
GPU vs TPU
GPU and TPU 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 TPU
TPU comes up when the question is fundamentally about infrastructure.
Gemini trained on TPU v5p pods.
Frequently asked
What is the difference between GPU and TPU?
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. TPU: TPUs are Google's custom AI accelerators, designed specifically for the matrix and reduction operations of neural networks. Used to train Gemini and large parts of Google's AI stack.
When should I use GPU vs TPU?
GPU is the right concept when you are focused on infrastructure. TPU applies when you are focused on infrastructure.
Are GPU and TPU the same thing?
No. GPU is infrastructure; TPU is infrastructure. They are related but address different parts of the AI stack.