Latency is the silent killer of LLM applications. A delay of even a few hundred milliseconds can erode user trust in conversational interfaces, stall agentic workflows, and inflate compute costs under load. Optimizing latency requires a systems-level approach: tightening the client, refining the prompt, selecting the right model, and choosing infrastructure that does not introduce unpredictable overhead. This guide covers practical strategies you can implement today, with concrete code examples and a look at how Oxlo.ai eliminates common infrastructure bottlenecks.
Understanding LLM Latency
LLM latency is not a single number. It is the sum of network transit, queue time, input processing (prefill), and token generation. The metrics that matter are:
- Time to First Byte (TTFB): The interval between your request and the first generated token. This includes network round-trips, authentication, and prefill computation.
- Inter-token Latency: The gap between consecutive tokens in a streaming response. Lower inter-token latency creates a fluid, conversational feel.
- Total Generation Time: The full duration from request to final token, determined by output length and decoding speed.
Optimizing one without considering the others can lead to suboptimal results. Streaming, for example, improves perceived TTFB but does not change total generation time.
Client-Side Optimizations
Before tuning the model
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