How to Reduce LLM Latency by 40% in Production

Latency is the silent killer of LLM adoption. A model that delivers brilliant answers in four seconds will lose users to a mediocre model that responds in under one second. For production deployments, reducing time-to-first-token and overall response latency is a core product requirement.

Understanding Where Latency Lives

LLM inference latency has three phases: preprocessing, the prefill phase (processing the full prompt), and the decode phase (generating tokens one by one). Most optimization focuses on decode because it dominates latency for long responses, but prefill dominates for long prompts with short outputs such as classification. Instrument your stack to measure each phase independently before optimizing.

Prompt Caching

Prompt caching is the highest-leverage optimization for applications with repeated context. If your application prepends a long system prompt or document to every request, caching the KV state for that prefix eliminates prefill cost entirely for subsequent requests. Structure prompts so stable content appears first and dynamic content follows the cached prefix.

KV Cache Optimization

Paged attention eliminates KV cache memory fragmentation by managing cache in fixed-size pages. This allows more concurrent requests within the same GPU memory envelope, reducing queuing latency. For multi-turn applications, prefix caching reuses cached states across turns rather than reprocessing the full conversation history on each request.

Speculative Decoding

Speculative decoding uses a small draft model to propose multiple tokens at once, verified in parallel by the larger target model. Speedups of 2-3x are achievable on predictable output tasks such as code completion and formulaic responses. A draft model roughly 10x smaller than the target model typically hits the right balance of speed and acceptance rate.

Quantization

INT8 quantization typically delivers 1.5-2x latency improvement with minimal quality degradation. INT4 delivers further gains but requires careful evaluation on your task distribution. Hardware selection should optimize for memory bandwidth rather than peak FLOPS, as LLM decode is memory-bandwidth-bound.