How to Reduce LLM Latency: Streaming, Caching, and Model Choice

Latency is the silent killer of LLM products. Users forgive a slow search box; they do not forgive a chat cursor that spins for four seconds before a single token appears. The good news is that most of the latency you feel is fixable with the right combination of transport, model choice, caching, and batching.

This guide breaks LLM latency into its components — time to first token and total generation time — then shows which levers move each one and what each lever costs you in accuracy or price.

Time to first token: the part users actually feel

Perceived responsiveness is dominated by time to first token (TTFT), not total generation. Streaming a response and rendering tokens as they arrive can cut perceived latency from seconds to something that feels instant, even when total generation time is unchanged. If you are not streaming to your UI, that is the single biggest perceived-latency win available.

Model choice beats infrastructure tuning

• Smaller models are dramatically faster: a 7B to 13B parameter model can generate two to four times more tokens per second than a frontier model on the same hardware.
• Route simple requests to a small model and reserve the frontier model for genuinely hard tasks — most traffic does not need frontier reasoning.
• Provider choice matters: identical model names differ in TTFT and throughput across providers, and peak-hour queuing can double latency.

Prompt caching for repeated prefixes

If every request starts with a long system prompt, instructions, or a few thousand tokens of shared context, you are recomputing the same prefix attention over and over. Prompt caching stores that computation, cutting both latency and cost on the cached prefix — typically the biggest single win for chat apps with long system prompts.

Batching, parallelism, and concurrency

API-level latency includes queue time. If you fan out N requests in a loop instead of concurrently, you serialize them and multiply your worst case. Parallelize independent calls, and for offline workloads — summarization, classification, enrichment — use the batch API, which trades wall-clock time for large price discounts rather than latency.

Geographic routing and connection quality

• Route API traffic to the provider region nearest your users — even 100 milliseconds of network round trip is visible on short requests.
• Reuse connections (HTTP keep-alive or gRPC) instead of re-handshaking per request.
• Do not parse the full response server-side before sending it to the client; stream the provider's chunks straight through your gateway.

Measure before you optimize

1. Instrument TTFT and time-to-last-token per model, per provider, and per prompt shape.
2. Set a latency budget per feature (for example, TTFT under 500 milliseconds, total under four seconds) and alert when p95 breaks it.
3. Re-measure after every model or provider change — latency shifts between versions.

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