Comparison
GPU vs Inference
GPU and Inference 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 Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
Frequently asked
What is the difference between GPU and Inference?
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. Inference: Inference is what happens when you actually run a trained model on new input. For LLMs that means generating tokens one at a time, with sampling and a KV cache.
When should I use GPU vs Inference?
GPU is the right concept when you are focused on infrastructure. Inference applies when you are focused on inference.
Are GPU and Inference the same thing?
No. GPU is infrastructure; Inference is inference. They are related but address different parts of the AI stack.