React Native AI Performance: Achieving 45ms Latency

By Malik Chohra

In mobile applications, an AI that takes three seconds to respond feels broken. Users have been trained by TikTok and Instagram to expect sub-100ms reactions. At CasaInnov, we obsess over Time to First Token (TTFT). In this tutorial, we outline the exact architectural choices we make in React Native to drop AI generation latency down to a blistering 45ms.

Why 500ms Feels Broken in Mobile AI

When you type a message to ChatGPT on the web, a 1-second delay is acceptable. The user is sitting at a desk. On a mobile device, users interact via short, rapid taps. If they tap a button to generate an AI summary and the screen freezes for 500ms waiting for the API, they will tap the button again, triggering a duplicate network call and potentially crashing the flow.

The latency pipeline is composed of three bottlenecks: the network handshake, the LLM TTFT, and the React Native rendering bridge. We must attack all three.

Step 1: Ditching HTTP for WebSockets / SSE

Standard HTTP POST requests require establishing a new TCP connection, performing TLS handshakes, and waiting. Even with HTTP Keep-Alive, streaming over standard REST in React Native can bottleneck due to how the JS engine Polyfills the fetch() stream reader.

• Server-Sent Events (SSE): Supported nicely in Expo SDK 52+, SSE maintains an open unidirectional pipe. It eliminates handshake overhead for subsequent requests.
• WebSockets: For real-time voice or intense chat UIs, establishing a WebSocket connection on app startup means TTFT is purely limited by the LLM and the speed of light.

Step 2: Bypassing the Bridge with JSI (Or New Architecture)

If you are receiving 50 tokens per second from an LLM, and you trigger a React state update for every single token, you are sending 50 messages per second across the asynchronous React Native bridge. The UI will stutter, and scroll performance will drop to 10 FPS.

The Solution: React Native New Architecture (Fabric) or JSI.

Using the New Architecture enables synchronous communication between JS and Native. Alternatively, we use aggressive JS-side buffering. We batch tokens into 50ms chunks. Instead of 50 state updates a second, React processes 20 smooth updates.

Step 3: Predictive UI & Skeleton Streaming

Perceived latency is just as important as actual latency. If you cannot get the physical network request under 150ms, you must manipulate the user's perception of time.

// React Native Predictive UI Concept
function AIChatBubble({ isGenerating }) {
  if (isGenerating && !firstTokenArrived) {
    return (
      <Animated.View style={styles.shimmer}>
        <Text style={styles.thinkingText}>AI is thinking...</Text>
      </Animated.View>
    );
  }
  return <Text>{content}</Text>;
}

By instantly rendering a smooth, 60fps native animation the exact millisecond the user taps the screen, their brain registers immediate feedback. By the time the animation completes its first cycle (approx 300ms), your WebSockets have delivered the first token, and the transition is clean.

Measuring TTFT (Time To First Token)

You cannot optimize what you do not measure. We inject telemetry into our AI hooks to measure the exact millisecond differential between the onPress event and the rendering of the first token in the Text node.

By using Edge functions (e.g., Cloudflare Workers sitting physically close to the Anthropic API servers) and caching deterministic queries, we reliably hit a 45ms median TTFT on 5G networks in major US/EU cities.

Conclusion

Dropping AI latency in React Native requires moving past fetch() and diving into the Native rendering pipeline. If you want to achieve "Apple-quality" AI interactions, WebSockets, JSI/Fabric, and aggressive Optimistic UI are non-negotiable.