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What is token streaming in LLM inference?
Token streaming delivers a model's output incrementally, one token at a time, as it is generated — rather than returning the whole answer at once. It makes applications feel responsive because users start reading immediately, and it's essential for chat and agent experiences.
How it works
Because an LLM generates output one token at a time, the server can send each token to the client as soon as it's produced instead of buffering the entire response. The time until the first token appears is time-to-first-token, and the rate at which the rest arrives is the per-output-token speed. Together they determine how a streamed answer feels to read.
Why it matters for user experience
Without streaming, a user stares at a blank screen until the whole answer is done. With streaming, text appears right away and keeps flowing, which feels dramatically faster even when total time is unchanged. For chatbots and agents that produce long answers, streaming is the difference between feeling instant and feeling stuck.
How Netra helps
Netra Runtime supports streaming through its OpenAI-compatible API. In a published benchmark at 256 concurrent users, Netra streamed tokens about 5.5x faster than vLLM — a new token every 7.4 ms versus 41 ms — so answers visibly type out several times faster under load. See inference latency and throughput or LLM inference for AI agents.
Frequently asked questions
What is the benefit of token streaming?
Streaming lets users start reading the answer as soon as the first token is ready, so the application feels responsive even when the full response takes time to finish.
How is streaming related to time-to-first-token?
Time-to-first-token is how long until streaming begins; the per-token speed after that determines how quickly the rest of the answer appears. Both shape how fast a streamed response feels.
Does Netra Runtime support streaming?
Yes. Netra Runtime supports streaming responses through its OpenAI-compatible API, so applications can display generated tokens progressively.