To optimize parallel processing in Azure OpenAI for better performance, you can consider the following best practices:
- Batch processing: By batching similar requests together, you can take advantage of parallel processing capabilities and maximize the efficiency of Azure OpenAI Service. This approach not only improves performance but also helps minimize costs by reducing the number of individual API calls.
- Model selection: The choice of model can significantly impact latency. For use cases that require the lowest latency models with the fastest response times, it is recommended to use the latest models in the GPT-3.5 Turbo model series.
- Generation size and Max tokens: Reducing the number of tokens generated per request can help reduce the latency of each request. You can set the max_token parameter on each call as small as possible, include stop sequences to prevent generating extra content, and generate fewer responses by adjusting the best_of & n parameters.
- Streaming: Streami