What is Streaming Inference?
TL;DR
An inference method that delivers AI model output in real-time as it is generated. Improves user experience.
Streaming Inference: Definition & Explanation
Streaming Inference is an inference method that delivers text and data generated by an AI model to the user incrementally in real-time, without waiting for the complete generation to finish. The familiar behavior of text appearing character by character in ChatGPT and Claude interfaces is a prime example of streaming inference. It is implemented using technologies such as Server-Sent Events (SSE) and WebSocket. The primary benefit is improved perceived speed. While LLM long-text generation can take seconds to tens of seconds, displaying output from the moment the first token is generated significantly reduces perceived wait time. TTFT (Time to First Token) is a key performance metric. By strategically combining streaming inference with batch inference, both cost efficiency and user experience can be optimized.