Real-time applications, from live coding assistants to conversational voice agents, require LLM latency measured in hundreds of milliseconds, not seconds. Achieving this consistently demands more than a fast model weights file. It requires a systems-level approach that spans model selection, serving infrastructure, client integration, and cost controls. This guide covers the concrete techniques that reduce time-to-first-token (TTFT) and inter-token latency, and where Oxlo.ai fits into a low-latency stack.
Define Strict Latency Budgets
Before optimizing, instrument your end-to-end pipeline. Real-time user experiences usually require TTFT under 200 ms and inter-token latency under 50 ms. Measure these from the client perspective, including network round trips and serialization overhead. Set budgets per model tier: a code-completion assistant has tighter constraints than a long-form reasoning agent.
Choose Models for Speed, Not Just Benchmarks
Parameter count is the strongest predictor of prefill and decode latency. For real-time workloads, prefer mid-size models or efficient Mixture-of-Experts (MoE) architectures over dense hundreds-of-billion-parameter variants.
