Comparison
Top-k vs Top-p
Top-k 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 Top-k
Top-k comes up when the question is fundamentally about inference.
top-k = 50: a common default in Hugging Face generation.
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 Top-k and Top-p?
Top-k: Top-k restricts token sampling to the k highest-probability tokens, then samples from that set. A simpler alternative to top-p. 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 Top-k vs Top-p?
Top-k is the right concept when you are focused on inference. Top-p applies when you are focused on inference.
Are Top-k and Top-p the same thing?
No. Top-k is inference; Top-p is inference. They are related but address different parts of the AI stack.