Comparison
Beam Search vs Inference
Beam Search and Inference are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Beam Search
Beam Search comes up when the question is fundamentally about inference.
Translation systems with beam width 4-10.
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 Beam Search and Inference?
Beam Search: Beam search explores several candidate continuations in parallel, keeping the top-k partial sequences at each step. Common in translation; rare in modern LLM chat. 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 Beam Search vs Inference?
Beam Search is the right concept when you are focused on inference. Inference applies when you are focused on inference.
Are Beam Search and Inference the same thing?
No. Beam Search is inference; Inference is inference. They are related but address different parts of the AI stack.