Comparison
Inference vs Top-p
Inference and Top-p are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
When you would reach for Top-p
Top-p comes up when the question is fundamentally about inference.
top-p = 0.9: typical for chat assistants.
Frequently asked
What is the difference between Inference and Top-p?
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. Top-p: Top-p (nucleus sampling) restricts token selection to the smallest set of tokens whose cumulative probability reaches p. Common values are 0.9-0.95.
When should I use Inference vs Top-p?
Inference is the right concept when you are focused on inference. Top-p applies when you are focused on inference.
Are Inference and Top-p the same thing?
No. Inference is inference; Top-p is inference. They are related but address different parts of the AI stack.