Streaming Inference

Unlike batch processing where the entire response is returned at once, streaming inference transmits each generated token sequentially over a persistent WebSocket or Server-Sent Events (SSE) connection. This mechanism allows the client to begin rendering text immediately, creating the illusion of real-time thought. The underlying transport typically uses chunked transfer encoding or a continuous data stream, where each payload contains a single token or a small group of tokens along with metadata like log probabilities.

Time-to-First-Token (TTFT) is the critical latency metric measuring the delay between submitting a query and receiving the first generated token. Streaming inference directly addresses perceived latency by minimizing TTFT. Key optimization techniques include:

• KV-Cache preloading: Pre-computing key-value pairs for system prompts
• Speculative decoding: Using a draft model to predict tokens ahead of the main model
• Prefix caching: Reusing computed states for overlapping prompt prefixes

Continuous batching is a serving technique that dynamically adds new sequences to a running batch and removes completed ones without waiting for all sequences to finish. This is essential for streaming inference because it prevents short responses from being blocked by long generations. The scheduler operates at the iteration level rather than the request level, maximizing GPU utilization by filling compute gaps with new requests as soon as a sequence slot frees up.

Streaming systems must implement backpressure mechanisms to handle mismatches between generation speed and client consumption. If the model generates tokens faster than the network can transmit or the client can render, buffers can overflow. Common strategies include:

• TCP flow control: Relying on transport-layer windowing
• Application-level buffering: Dropping or coalescing tokens when queues exceed thresholds
• Adaptive generation: Pausing inference when the client signals it is overwhelmed

A defining characteristic of streaming inference is the ability for users to interrupt generation mid-stream. When a user clicks 'stop' or submits a new query, the server must immediately halt token production and release allocated resources. This requires the inference engine to support cooperative cancellation at the iteration level, terminating the autoregressive loop without waiting for the end-of-sequence token. Proper cancellation prevents wasted GPU compute and ensures responsive multi-turn conversations.

Streaming inference extends beyond plain text to structured outputs like JSON. Instead of waiting for the full object to validate, the server streams partial JSON tokens while enforcing a constrained grammar or JSON schema in real-time. This enables progressive rendering of structured data in user interfaces. Techniques include:

• Incremental parsing: Validating partial JSON on each token
• Grammar-constrained decoding: Masking invalid next tokens at each step
• Type-ahead streaming: Sending type information before values

A critical latency metric measuring the delay between submitting a query and receiving the first generated token from the inference engine. TTFT is the primary user-perceived performance indicator for streaming inference systems. It encompasses input tokenization, prompt processing through the prefill phase, and the initial decoding step. Target TTFT values for real-time applications typically fall below 200 milliseconds. Optimizing TTFT involves KV-cache precomputation, speculative decoding, and prompt prefix caching to minimize the prefill bottleneck.

A latency-reduction technique that uses a smaller, faster draft model to generate multiple candidate tokens in parallel, which are then verified and accepted or rejected by the larger target model in a single forward pass. This accelerates streaming inference by reducing the number of sequential decoding steps required from the large model. The draft model proposes K tokens; the target model checks them simultaneously. Accepted tokens are streamed immediately, while rejected tokens trigger a fallback generation. This can yield 2-3x speedups in tokens-per-second without quality degradation.