Streaming Inference

Large language models generate text one token at a time in an autoregressive loop — each token is computed from all previous tokens. Without streaming, the client waits until every token in a response is generated before receiving anything, which for a 500-token response at 30 tokens/second means a 16-second blank screen. With streaming, the first tokens arrive in under a second and the UI populates progressively, matching the user's reading speed rather than their waiting patience.

Technically, streaming inference is implemented using Server-Sent Events (SSE) or WebSockets at the transport layer, with the model server flushing each generated token (or a small batch of tokens) to the client immediately. Most enterprise inference platforms — all major MaaS APIs, vLLM, Triton, and LiteLLM — support streaming natively. The application layer must be built to handle streamed responses: the UI must progressively render partial output, and the backend must correctly propagate the stream from the model server through any middleware layers without buffering.

The enterprise architecture consideration is end-to-end stream propagation. A streaming model API delivers no benefit if an intermediate API gateway, load balancer, or application server buffers the entire response before forwarding it. Each layer in the serving path must be configured for streaming: HTTP/2 or chunked transfer encoding, SSE or WebSocket-compatible gateway rules, and frontend frameworks that handle streaming state correctly. Monitoring time-to-first-token (TTFT) as a first-class SLO — separate from total response latency — is the operational signal that confirms streaming is delivering its intended UX benefit.