Streaming Inference

Why It Matters

Streaming inference transforms the user experience of LLM applications. Without streaming, users stare at a loading spinner for 5-30 seconds while the model generates a complete response. With streaming, they see tokens appear in real-time, similar to watching someone type, psychologically more engaging and providing immediate feedback that the system is working.

For production AI applications, streaming also enables early termination. If a user realizes the model is going in the wrong direction, they can stop generation immediately rather than waiting for a complete (useless) response. This saves compute costs and improves user satisfaction.

Streaming adds implementation complexity but is expected in chat-based AI interfaces. Users accustomed to ChatGPT’s streaming experience will perceive non-streaming interfaces as broken or slow, even if the total response time is identical.

Implementation Basics

How streaming works:

LLMs generate tokens sequentially. Each token depends on previous tokens. Streaming sends each token to the client immediately after generation instead of buffering the complete response. The protocol is typically Server-Sent Events (SSE) over HTTP.

Implementing streaming with popular APIs:

• OpenAI/Claude: Set stream=True in API calls, iterate over response chunks
• FastAPI: Use StreamingResponse with async generators
• Frontend: EventSource API or fetch with ReadableStream

Key implementation considerations:

• Connection handling: Long-lived connections require timeout management
• Error handling: Partial responses need graceful failure modes
• Rate limiting: Per-token or per-request limits affect streaming differently
• Caching: Streaming responses are harder to cache than complete responses

Streaming architecture patterns:

• Direct passthrough: Proxy model API streams directly to client
• Transform streams: Process tokens (format, filter) before forwarding
• Aggregate with stream: Store complete response while streaming to client

Performance optimization:

• Keep connections warm to avoid handshake latency
• Use HTTP/2 for better multiplexing of concurrent streams
• Implement backpressure if clients can’t consume tokens fast enough
• Consider WebSockets for bidirectional streaming requirements

Most LLM API clients handle streaming details. Your job is piping the stream through your backend to your frontend while handling errors and connection lifecycle appropriately.