Real-Time LLM Applications: A Step-by-Step Guide

Real-time LLM applications are no longer a niche experiment. From live coding assistants to conversational voice agents, users expect responses to appear within hundreds of milliseconds, not seconds. Building these systems requires more than a fast model. It demands careful attention to networking, streaming architecture, context management, and inference infrastructure that can sustain consistent latency under unpredictable load. This guide walks through the core engineering decisions and implementation patterns that separate sluggish demos from production-grade real-time experiences.

What Makes an LLM Application "Real-Time"

Latency in LLM applications is usually measured by two metrics: time to first token (TTFT) and inter-token latency (ITL). For an interaction to feel instantaneous, TTFT should typically stay under 300 ms and ITL should remain low enough that text renders as a smooth stream rather than staccato bursts. Achieving this requires full-duplex streaming, efficient prompt processing, and infrastructure without cold starts that can add seconds of unpredictable delay to every session.

Architectural Patterns for Sub-Second Inference

The transport layer matters. For browser-based clients, Server-Sent Events (SSE) over HTTP/2 give you a unidirectional stream with automatic reconnection and broad compatibility. WebSockets are useful for bidirectional binary traffic, such as audio bytes in a voice agent, but introduce extra connection management overhead. Regardless of protocol, keep connections warm. Repeated TLS handshakes and DNS lookups destroy latency budgets, so reuse HTTP sessions via connection pooling.

On the server, use an async runtime. Blocking the event loop while waiting for the model to generate the first token will stall every concurrent user. The example below shows a minimal Python async generator that consumes an SSE stream and yields tokens as they arrive.

import asyncio
import os
from openai import AsyncOpenAI

client = AsyncOpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

async def stream_tokens(prompt: str):
    response = await client.chat.completions.create(
        model="deepseek-v4-flash",
        messages=[{"role": "user", "content": prompt}],
        stream=True
    )
    async for chunk in response:
        token = chunk.choices[0].delta.content
        if token:
            yield token

Choosing the Right Model and Infrastructure

Model size directly impacts inference speed. A 70B parameter model offers high quality but requires substantial throughput optimization for real-time use, while smaller models or mixture-of-experts architectures can deliver strong reasoning