Latency is the most common reason AI features fail to retain users. When response times stretch past a few hundred milliseconds, engagement drops and trust erodes. For large language models, latency is not a single number. It is a stack of bottlenecks spanning model architecture, serving infrastructure, and client-side integration. Optimizing each layer requires understanding how inference actually executes, then making concrete tradeoffs between speed, quality, and cost.
Understanding Inference Latency
LLM inference has two distinct phases: prefill and decode. During prefill, the model processes the entire input prompt in parallel to build the key-value cache. This phase determines Time To First Token, or TTFT. During decode, the model generates tokens one at a time in an autoregressive loop. This phase determines Time Per Output Token, or TPOT, and it dominates total latency for long responses.
TTFT is primarily compute-bound and scales with prompt length. TPOT is usually memory-bandwidth-bound because each new token requires reading the full model weights and the growing KV cache. If your application feels slow, identify which phase is the culprit before choosing an optimization.
Model Selection and Quantization
The simplest way to reduce latency is to use a smaller, distilled, or quantized model that still meets your quality bar. A 7B parameter model at INT4 will have significantly lower memory bandwidth pressure than a 70B model at FP16, and for many classification, extraction, or routing tasks the accuracy difference is negligible.
Oxlo.ai hosts a spectrum of open-source models that let you make this tradeoff without rewriting client code. For latency-sensitive coding tasks, Oxlo.ai Coder
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