# How do I address memory or performance issues on my client side when handling very large responses returned by Bedrock models?

Source: https://zilliz.com/ai-faq/how-do-i-address-memory-or-performance-issues-on-my-client-side-when-handling-very-large-responses-returned-by-bedrock-models
Author: zilliz

To address memory or performance issues when handling large Bedrock model responses on the client side, focus on optimizing data handling, reducing unnecessary processing, and leveraging efficient client-side techniques. Here's a structured approach:

**1. Stream Responses Incrementally** 
Instead of waiting for the entire response to load, process data in chunks. For example, use Bedrock’s streaming API (if supported) or implement client-side pagination. With streaming, you can handle data as it arrives—like displaying partial results in a UI or parsing tokens incrementally—which prevents holding the entire payload in memory. For HTTP-based implementations, use the `fetch` API with `ReadableStream` to process chunks as they arrive. This reduces memory spikes and keeps the UI responsive. For example: 
```javascript
const response = await fetch(endpoint);
const reader = response.body.getReader();
while (true) {
 const { done, value } = await reader.read();
 if (done) break;
 // Process each chunk (e.g., update UI or buffer)
}
```

**2. Optimize Data Formats and Processing** 
Large JSON payloads are a common bottleneck. Use compression (e.g., gzip via `Content-Encoding`) to reduce transfer size, and parse data efficiently. Avoid converting large responses into in-memory objects—parse selectively using streaming JSON parsers like `JSON.parse` with chunked input or libraries like `oboe.js`. For repetitive data, use typed arrays (e.g., `Uint8Array`) instead of standard arrays to reduce overhead. If the response includes unnecessary metadata, request a trimmed version from the server or filter fields early in the processing pipeline. For example, truncate or discard unused portions of the response immediately after receipt.

**3. Implement Caching and Memory Management** 
Cache repeated or static portions of responses using browser storage (e.g., `localStorage` or `IndexedDB`) to avoid reprocessing. For dynamic content, use weak references (`WeakMap`/`WeakSet`) to allow garbage collection of unused data. Monitor memory usage with browser tools like Chrome DevTools’ Memory tab to identify leaks—for instance, detached DOM elements or uncleared event listeners in single-page apps. If memory constraints persist, offload processing to Web Workers to avoid blocking the main thread. Additionally, consider server-side optimizations: limit response size via Bedrock API parameters (e.g., `maxTokens`) or use smaller models for tasks where precision isn’t critical.