Comparison
Inference vs Time to First Token
Inference and Time to First Token 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 Time to First Token
Time to First Token comes up when the question is fundamentally about inference.
Claude with a 50K-token cached prefix: TTFT drops from ~6s to under 1s on subsequent calls reusing the same prefix.
Frequently asked
What is the difference between Inference and Time to First Token?
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. Time to First Token: Time to first token (TTFT) is how long it takes from sending a request until the first response token arrives. The user-perceived latency metric for streaming chat.
When should I use Inference vs Time to First Token?
Inference is the right concept when you are focused on inference. Time to First Token applies when you are focused on inference.
Are Inference and Time to First Token the same thing?
No. Inference is inference; Time to First Token is inference. They are related but address different parts of the AI stack.