Comparison
Streaming (LLM Responses) vs Time per Output Token
Streaming (LLM Responses) and Time per Output Token are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Streaming (LLM Responses)
Streaming (LLM Responses) comes up when the question is fundamentally about inference.
A ChatGPT-style web app: SSE stream rendering tokens as they arrive, TTFT ~0.6s vs full-wait of ~5s.
When you would reach for Time per Output Token
Time per Output Token comes up when the question is fundamentally about inference.
A 70B model on H100: TPOT ~25ms (~40 tokens/sec).
Frequently asked
What is the difference between Streaming (LLM Responses) and Time per Output Token?
Streaming (LLM Responses): Streaming returns tokens to the client as they're generated rather than holding the full response until completion. Implemented over Server-Sent Events (SSE) or WebSocket; what makes chat UIs feel fast. Time per Output Token: Time per output token (TPOT) is the average wall-clock delay between consecutive generated tokens during streaming. Determines how fast text appears once generation starts.
When should I use Streaming (LLM Responses) vs Time per Output Token?
Streaming (LLM Responses) is the right concept when you are focused on inference. Time per Output Token applies when you are focused on inference.
Are Streaming (LLM Responses) and Time per Output Token the same thing?
No. Streaming (LLM Responses) is inference; Time per Output Token is inference. They are related but address different parts of the AI stack.