Google Cloud published a guide on handling 429 "resource exhaustion" errors, particularly when working with Large Language Models (LLMs). The article emphasizes managing resource consumption for a smooth user experience, given LLMs' substantial computational demands. It presents three key strategies:
1. **Backoff and Retry:** Implement exponential backoff and retry logic to handle resource exhaustion or API unavailability. Waiting time increases exponentially with each retry until the overloaded system recovers.
2. **Dynamic Shared Quota:** Google Cloud manages resource allocation for certain models by dynamically distributing available capacity among users making requests. This improves efficiency and reduces latency.
3. **Provisioned Throughput:** This service lets you reserve dedicated capacity for generative AI models on Vertex AI, ensuring predictable performance even during peak demand.
The article highlights combining backoff/retry with dynamic shared quota, especially as request volume and token size grow. Other options like consumer quota override and provisioned throughput are mentioned for LLM application resilience. It encourages building with generative AI using Vertex AI samples on GitHub or leveraging Google Cloud's beginner guide, quickstarts, or starter pack.
